{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/Misconduct_ISQ_replication_log.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 7 Jun 2019, 19:49:56

{com}. ****PKAT PKO misconduct replication models

. 
. ***CAT1_SEA_MIL Models, reported in Table 1

. 
. *Model 1

. nbreg CAT1_SEA_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_SIZE MANDATE3 POP_DENSITY, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-509.11396}  
Iteration 1:{space 3}log pseudolikelihood = {res:-492.38579}  
Iteration 2:{space 3}log pseudolikelihood = {res:-492.28759}  
Iteration 3:{space 3}log pseudolikelihood = {res:-492.28756}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-404.89038}  
Iteration 1:{space 3}log pseudolikelihood = {res:-356.12068}  
Iteration 2:{space 3}log pseudolikelihood = {res:-356.11763}  
Iteration 3:{space 3}log pseudolikelihood = {res:-356.11763}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-346.86746}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-325.31398}  
Iteration 2:{space 3}log pseudolikelihood = {res:-310.56259}  
Iteration 3:{space 3}log pseudolikelihood = {res:-296.40664}  
Iteration 4:{space 3}log pseudolikelihood = {res:-295.49962}  
Iteration 5:{space 3}log pseudolikelihood = {res:  -295.494}  
Iteration 6:{space 3}log pseudolikelihood = {res:  -295.494}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     66.02
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}  -295.494{txt}{col 49}Pseudo R2{col 67}= {res}    0.1702

{txt}{ralign 81:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}   CAT1_SEA_MIL{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
PTS_MISSION_MIL {c |}{col 17}{res}{space 2} .5508466{col 29}{space 2} .3317989{col 40}{space 1}    1.66{col 49}{space 3}0.097{col 57}{space 4}-.0994673{col 70}{space 3}  1.20116
{txt}{space 1}DEMOCRATIC_MIL {c |}{col 17}{res}{space 2} .1985354{col 29}{space 2} .3055675{col 40}{space 1}    0.65{col 49}{space 3}0.516{col 57}{space 4}-.4003659{col 70}{space 3} .7974368
{txt}{space 5}GENDER_MIL {c |}{col 17}{res}{space 2}-9.746283{col 29}{space 2} 8.844178{col 40}{space 1}   -1.10{col 49}{space 3}0.270{col 57}{space 4}-27.08055{col 70}{space 3} 7.587988
{txt}{space 5}FORCE_SIZE {c |}{col 17}{res}{space 2} .0001678{col 29}{space 2} .0000404{col 40}{space 1}    4.15{col 49}{space 3}0.000{col 57}{space 4} .0000886{col 70}{space 3}  .000247
{txt}{space 7}MANDATE3 {c |}{col 17}{res}{space 2} .1306455{col 29}{space 2} .3873992{col 40}{space 1}    0.34{col 49}{space 3}0.736{col 57}{space 4} -.628643{col 70}{space 3}  .889934
{txt}{space 4}POP_DENSITY {c |}{col 17}{res}{space 2} .0012782{col 29}{space 2} .0016566{col 40}{space 1}    0.77{col 49}{space 3}0.440{col 57}{space 4}-.0019687{col 70}{space 3}  .004525
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-1.455779{col 29}{space 2} .7550253{col 40}{space 1}   -1.93{col 49}{space 3}0.054{col 57}{space 4}-2.935601{col 70}{space 3} .0240434
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/lnalpha {c |}{col 17}{res}{space 2} .0489061{col 29}{space 2} .2112925{col 57}{space 4}-.3652196{col 70}{space 3} .4630317
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          alpha {c |}{col 17}{res}{space 2} 1.050122{col 29}{space 2} .2218828{col 57}{space 4} .6940442{col 70}{space 3} 1.588884
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 2

. nbreg CAT1_SEA_MIL PTS_MISSION_MIL GDP_MISSION_MIL GENDER_MIL FORCE_SIZE MANDATE3 POP_DENSITY, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-502.37592}  
Iteration 1:{space 3}log pseudolikelihood = {res:-482.15134}  
Iteration 2:{space 3}log pseudolikelihood = {res:-481.80959}  
Iteration 3:{space 3}log pseudolikelihood = {res: -481.8086}  
Iteration 4:{space 3}log pseudolikelihood = {res: -481.8086}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-404.89038}  
Iteration 1:{space 3}log pseudolikelihood = {res:-356.12068}  
Iteration 2:{space 3}log pseudolikelihood = {res:-356.11763}  
Iteration 3:{space 3}log pseudolikelihood = {res:-356.11763}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-343.20051}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-321.49459}  
Iteration 2:{space 3}log pseudolikelihood = {res:-297.80332}  
Iteration 3:{space 3}log pseudolikelihood = {res:-294.71504}  
Iteration 4:{space 3}log pseudolikelihood = {res:-294.60263}  
Iteration 5:{space 3}log pseudolikelihood = {res:-294.60253}  
Iteration 6:{space 3}log pseudolikelihood = {res:-294.60253}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     68.01
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-294.60253{txt}{col 49}Pseudo R2{col 67}= {res}    0.1727

{txt}{ralign 81:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}   CAT1_SEA_MIL{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
PTS_MISSION_MIL {c |}{col 17}{res}{space 2} .4909015{col 29}{space 2}  .303711{col 40}{space 1}    1.62{col 49}{space 3}0.106{col 57}{space 4}-.1043612{col 70}{space 3} 1.086164
{txt}GDP_MISSION_MIL {c |}{col 17}{res}{space 2}-.0000253{col 29}{space 2} .0000283{col 40}{space 1}   -0.89{col 49}{space 3}0.371{col 57}{space 4}-.0000809{col 70}{space 3} .0000302
{txt}{space 5}GENDER_MIL {c |}{col 17}{res}{space 2}-11.21501{col 29}{space 2} 8.791285{col 40}{space 1}   -1.28{col 49}{space 3}0.202{col 57}{space 4}-28.44561{col 70}{space 3} 6.015597
{txt}{space 5}FORCE_SIZE {c |}{col 17}{res}{space 2} .0001539{col 29}{space 2} .0000436{col 40}{space 1}    3.53{col 49}{space 3}0.000{col 57}{space 4} .0000685{col 70}{space 3} .0002393
{txt}{space 7}MANDATE3 {c |}{col 17}{res}{space 2} .0976489{col 29}{space 2} .3828207{col 40}{space 1}    0.26{col 49}{space 3}0.799{col 57}{space 4}-.6526658{col 70}{space 3} .8479636
{txt}{space 4}POP_DENSITY {c |}{col 17}{res}{space 2} .0013308{col 29}{space 2}  .001583{col 40}{space 1}    0.84{col 49}{space 3}0.401{col 57}{space 4}-.0017717{col 70}{space 3} .0044334
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.8388586{col 29}{space 2} 1.039614{col 40}{space 1}   -0.81{col 49}{space 3}0.420{col 57}{space 4}-2.876464{col 70}{space 3} 1.198747
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/lnalpha {c |}{col 17}{res}{space 2}-.0023401{col 29}{space 2} .2324649{col 57}{space 4} -.457963{col 70}{space 3} .4532827
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          alpha {c |}{col 17}{res}{space 2} .9976626{col 29}{space 2} .2319215{col 57}{space 4} .6325709{col 70}{space 3} 1.573469
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 3

. nbreg CAT1_SEA_MIL CORRUPTION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_SIZE MANDATE3 POP_DENSITY, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-485.92953}  
Iteration 1:{space 3}log pseudolikelihood = {res:-469.84435}  
Iteration 2:{space 3}log pseudolikelihood = {res:-469.68497}  
Iteration 3:{space 3}log pseudolikelihood = {res:-469.68479}  
Iteration 4:{space 3}log pseudolikelihood = {res:-469.68479}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-404.89038}  
Iteration 1:{space 3}log pseudolikelihood = {res:-356.12068}  
Iteration 2:{space 3}log pseudolikelihood = {res:-356.11763}  
Iteration 3:{space 3}log pseudolikelihood = {res:-356.11763}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-337.70184}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-315.16331}  
Iteration 2:{space 3}log pseudolikelihood = {res:-306.19837}  
Iteration 3:{space 3}log pseudolikelihood = {res:-292.92914}  
Iteration 4:{space 3}log pseudolikelihood = {res:-291.04745}  
Iteration 5:{space 3}log pseudolikelihood = {res:-291.02442}  
Iteration 6:{space 3}log pseudolikelihood = {res: -291.0244}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     93.24
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res} -291.0244{txt}{col 49}Pseudo R2{col 67}= {res}    0.1828

{txt}{ralign 80:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}  CAT1_SEA_MIL{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
CORRUPTION_MIL {c |}{col 16}{res}{space 2}-1.216374{col 28}{space 2} .4690126{col 39}{space 1}   -2.59{col 48}{space 3}0.010{col 56}{space 4}-2.135622{col 69}{space 3}-.2971264
{txt}DEMOCRATIC_MIL {c |}{col 16}{res}{space 2}-.0529226{col 28}{space 2}  .294552{col 39}{space 1}   -0.18{col 48}{space 3}0.857{col 56}{space 4}-.6302339{col 69}{space 3} .5243887
{txt}{space 4}GENDER_MIL {c |}{col 16}{res}{space 2}-9.898938{col 28}{space 2} 7.710107{col 39}{space 1}   -1.28{col 48}{space 3}0.199{col 56}{space 4}-25.01047{col 69}{space 3} 5.212593
{txt}{space 4}FORCE_SIZE {c |}{col 16}{res}{space 2} .0001644{col 28}{space 2} .0000313{col 39}{space 1}    5.25{col 48}{space 3}0.000{col 56}{space 4} .0001031{col 69}{space 3} .0002257
{txt}{space 6}MANDATE3 {c |}{col 16}{res}{space 2} .2405512{col 28}{space 2} .3420855{col 39}{space 1}    0.70{col 48}{space 3}0.482{col 56}{space 4}-.4299241{col 69}{space 3} .9110264
{txt}{space 3}POP_DENSITY {c |}{col 16}{res}{space 2} .0023586{col 28}{space 2} .0019036{col 39}{space 1}    1.24{col 48}{space 3}0.215{col 56}{space 4}-.0013723{col 69}{space 3} .0060895
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.5205031{col 28}{space 2} .4970119{col 39}{space 1}   -1.05{col 48}{space 3}0.295{col 56}{space 4}-1.494629{col 69}{space 3} .4536223
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}/lnalpha {c |}{col 16}{res}{space 2}-.1211448{col 28}{space 2}  .251591{col 56}{space 4}-.6142541{col 69}{space 3} .3719645
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
         alpha {c |}{col 16}{res}{space 2} .8859057{col 28}{space 2} .2228859{col 56}{space 4} .5410443{col 69}{space 3} 1.450581
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 4

. nbreg CAT1_SEA_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-941.83988}  
Iteration 1:{space 3}log pseudolikelihood = {res:-575.53962}  
Iteration 2:{space 3}log pseudolikelihood = {res:-567.09605}  
Iteration 3:{space 3}log pseudolikelihood = {res:-567.05054}  
Iteration 4:{space 3}log pseudolikelihood = {res:-567.05054}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-404.89038}  
Iteration 1:{space 3}log pseudolikelihood = {res:-356.12068}  
Iteration 2:{space 3}log pseudolikelihood = {res:-356.11763}  
Iteration 3:{space 3}log pseudolikelihood = {res:-356.11763}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-346.07002}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-328.21241}  
Iteration 2:{space 3}log pseudolikelihood = {res:  -319.179}  
Iteration 3:{space 3}log pseudolikelihood = {res:-312.32864}  
Iteration 4:{space 3}log pseudolikelihood = {res: -312.2561}  
Iteration 5:{space 3}log pseudolikelihood = {res:-312.25599}  
Iteration 6:{space 3}log pseudolikelihood = {res:-312.25599}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     57.76
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-312.25599{txt}{col 49}Pseudo R2{col 67}= {res}    0.1232

{txt}{ralign 86:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}        CAT1_SEA_MIL{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}PTS_MISSION_MIL {c |}{col 22}{res}{space 2} 1.151343{col 34}{space 2} .3497643{col 45}{space 1}    3.29{col 54}{space 3}0.001{col 62}{space 4} .4658176{col 75}{space 3} 1.836868
{txt}{space 6}DEMOCRATIC_MIL {c |}{col 22}{res}{space 2}-.0379812{col 34}{space 2} .2988585{col 45}{space 1}   -0.13{col 54}{space 3}0.899{col 62}{space 4} -.623733{col 75}{space 3} .5477706
{txt}{space 10}GENDER_MIL {c |}{col 22}{res}{space 2}-16.15391{col 34}{space 2} 8.856818{col 45}{space 1}   -1.82{col 54}{space 3}0.068{col 62}{space 4}-33.51295{col 75}{space 3} 1.205135
{txt}{space 7}FORCE_DENSITY {c |}{col 22}{res}{space 2} 1.473337{col 34}{space 2} .8511924{col 45}{space 1}    1.73{col 54}{space 3}0.083{col 62}{space 4}-.1949696{col 75}{space 3} 3.141643
{txt}PKO_FATALITIES_TOTAL {c |}{col 22}{res}{space 2} .0612265{col 34}{space 2} .0461934{col 45}{space 1}    1.33{col 54}{space 3}0.185{col 62}{space 4} -.029311{col 75}{space 3} .1517639
{txt}{space 12}MANDATE3 {c |}{col 22}{res}{space 2} .1528753{col 34}{space 2} .5410232{col 45}{space 1}    0.28{col 54}{space 3}0.778{col 62}{space 4}-.9075108{col 75}{space 3} 1.213261
{txt}{space 6}ln_KM2_country {c |}{col 22}{res}{space 2} .1153239{col 34}{space 2} .1617285{col 45}{space 1}    0.71{col 54}{space 3}0.476{col 62}{space 4}-.2016582{col 75}{space 3}  .432306
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-3.093769{col 34}{space 2} 1.961723{col 45}{space 1}   -1.58{col 54}{space 3}0.115{col 62}{space 4}-6.938675{col 75}{space 3} .7511381
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/lnalpha {c |}{col 22}{res}{space 2} .4078693{col 34}{space 2} .1947884{col 62}{space 4} .0260909{col 75}{space 3} .7896476
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} 1.503611{col 34}{space 2}  .292886{col 62}{space 4} 1.026434{col 75}{space 3}  2.20262
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 5

. nbreg CAT1_SEA_MIL PTS_MISSION_MIL GDP_MISSION_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-854.85164}  
Iteration 1:{space 3}log pseudolikelihood = {res:-547.18003}  
Iteration 2:{space 3}log pseudolikelihood = {res:-540.91385}  
Iteration 3:{space 3}log pseudolikelihood = {res:-540.89008}  
Iteration 4:{space 3}log pseudolikelihood = {res:-540.89008}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-404.89038}  
Iteration 1:{space 3}log pseudolikelihood = {res:-356.12068}  
Iteration 2:{space 3}log pseudolikelihood = {res:-356.11763}  
Iteration 3:{space 3}log pseudolikelihood = {res:-356.11763}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-348.25253}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res: -326.7177}  
Iteration 2:{space 3}log pseudolikelihood = {res:-312.53586}  
Iteration 3:{space 3}log pseudolikelihood = {res:-310.29695}  
Iteration 4:{space 3}log pseudolikelihood = {res:-310.16371}  
Iteration 5:{space 3}log pseudolikelihood = {res:-310.16341}  
Iteration 6:{space 3}log pseudolikelihood = {res:-310.16341}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     57.44
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-310.16341{txt}{col 49}Pseudo R2{col 67}= {res}    0.1290

{txt}{ralign 86:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}        CAT1_SEA_MIL{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}PTS_MISSION_MIL {c |}{col 22}{res}{space 2} .8295336{col 34}{space 2} .3798976{col 45}{space 1}    2.18{col 54}{space 3}0.029{col 62}{space 4} .0849479{col 75}{space 3} 1.574119
{txt}{space 5}GDP_MISSION_MIL {c |}{col 22}{res}{space 2}-.0000325{col 34}{space 2} .0000321{col 45}{space 1}   -1.01{col 54}{space 3}0.311{col 62}{space 4}-.0000954{col 75}{space 3} .0000304
{txt}{space 10}GENDER_MIL {c |}{col 22}{res}{space 2}-17.30007{col 34}{space 2} 8.678275{col 45}{space 1}   -1.99{col 54}{space 3}0.046{col 62}{space 4}-34.30918{col 75}{space 3}-.2909648
{txt}{space 7}FORCE_DENSITY {c |}{col 22}{res}{space 2} 1.272964{col 34}{space 2} .7621789{col 45}{space 1}    1.67{col 54}{space 3}0.095{col 62}{space 4}-.2208793{col 75}{space 3} 2.766807
{txt}PKO_FATALITIES_TOTAL {c |}{col 22}{res}{space 2}  .054322{col 34}{space 2} .0451182{col 45}{space 1}    1.20{col 54}{space 3}0.229{col 62}{space 4} -.034108{col 75}{space 3} .1427521
{txt}{space 12}MANDATE3 {c |}{col 22}{res}{space 2} .0914513{col 34}{space 2} .5388723{col 45}{space 1}    0.17{col 54}{space 3}0.865{col 62}{space 4}-.9647191{col 75}{space 3} 1.147622
{txt}{space 6}ln_KM2_country {c |}{col 22}{res}{space 2} .0360376{col 34}{space 2} .1445943{col 45}{space 1}    0.25{col 54}{space 3}0.803{col 62}{space 4} -.247362{col 75}{space 3} .3194372
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-.9147869{col 34}{space 2}  2.07187{col 45}{space 1}   -0.44{col 54}{space 3}0.659{col 62}{space 4}-4.975577{col 75}{space 3} 3.146003
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/lnalpha {c |}{col 22}{res}{space 2} .3310386{col 34}{space 2} .2327342{col 62}{space 4} -.125112{col 75}{space 3} .7871892
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} 1.392414{col 34}{space 2} .3240622{col 62}{space 4} .8823981{col 75}{space 3} 2.197212
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 6

. nbreg CAT1_SEA_MIL CORRUPTION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1058.0108}  
Iteration 1:{space 3}log pseudolikelihood = {res:-597.47131}  
Iteration 2:{space 3}log pseudolikelihood = {res:-584.95035}  
Iteration 3:{space 3}log pseudolikelihood = {res:-584.78955}  
Iteration 4:{space 3}log pseudolikelihood = {res:-584.78953}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-404.89038}  
Iteration 1:{space 3}log pseudolikelihood = {res:-356.12068}  
Iteration 2:{space 3}log pseudolikelihood = {res:-356.11763}  
Iteration 3:{space 3}log pseudolikelihood = {res:-356.11763}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-338.74697}  
Iteration 1:{space 3}log pseudolikelihood = {res:-336.75056}  (backed up)
Iteration 2:{space 3}log pseudolikelihood = {res: -316.4345}  
Iteration 3:{space 3}log pseudolikelihood = {res:-311.64964}  
Iteration 4:{space 3}log pseudolikelihood = {res: -311.6206}  
Iteration 5:{space 3}log pseudolikelihood = {res:-311.62059}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     93.22
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-311.62059{txt}{col 49}Pseudo R2{col 67}= {res}    0.1250

{txt}{ralign 86:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}        CAT1_SEA_MIL{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}CORRUPTION_MIL {c |}{col 22}{res}{space 2} -1.51664{col 34}{space 2} .3713437{col 45}{space 1}   -4.08{col 54}{space 3}0.000{col 62}{space 4}-2.244461{col 75}{space 3}  -.78882
{txt}{space 6}DEMOCRATIC_MIL {c |}{col 22}{res}{space 2}-.3691407{col 34}{space 2} .3089807{col 45}{space 1}   -1.19{col 54}{space 3}0.232{col 62}{space 4}-.9747318{col 75}{space 3} .2364503
{txt}{space 10}GENDER_MIL {c |}{col 22}{res}{space 2}-16.21121{col 34}{space 2} 7.408066{col 45}{space 1}   -2.19{col 54}{space 3}0.029{col 62}{space 4}-30.73075{col 75}{space 3}-1.691667
{txt}{space 7}FORCE_DENSITY {c |}{col 22}{res}{space 2} 1.831051{col 34}{space 2} .9162489{col 45}{space 1}    2.00{col 54}{space 3}0.046{col 62}{space 4} .0352363{col 75}{space 3} 3.626866
{txt}PKO_FATALITIES_TOTAL {c |}{col 22}{res}{space 2}  .066655{col 34}{space 2} .0444348{col 45}{space 1}    1.50{col 54}{space 3}0.134{col 62}{space 4}-.0204356{col 75}{space 3} .1537456
{txt}{space 12}MANDATE3 {c |}{col 22}{res}{space 2} .5214393{col 34}{space 2} .5293178{col 45}{space 1}    0.99{col 54}{space 3}0.325{col 62}{space 4}-.5160045{col 75}{space 3} 1.558883
{txt}{space 6}ln_KM2_country {c |}{col 22}{res}{space 2} .1490971{col 34}{space 2} .1573156{col 45}{space 1}    0.95{col 54}{space 3}0.343{col 62}{space 4}-.1592358{col 75}{space 3}   .45743
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-1.351084{col 34}{space 2} 1.973875{col 45}{space 1}   -0.68{col 54}{space 3}0.494{col 62}{space 4}-5.219807{col 75}{space 3} 2.517639
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/lnalpha {c |}{col 22}{res}{space 2} .3647814{col 34}{space 2} .2683633{col 62}{space 4} -.161201{col 75}{space 3} .8907638
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} 1.440199{col 34}{space 2} .3864966{col 62}{space 4}  .851121{col 75}{space 3}  2.43699
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 7

. nbreg CAT1_SEA_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_SIZE MANDATE3 GDP_HOST, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-479.70877}  
Iteration 1:{space 3}log pseudolikelihood = {res:-467.70669}  
Iteration 2:{space 3}log pseudolikelihood = {res:-467.19956}  
Iteration 3:{space 3}log pseudolikelihood = {res:  -467.196}  
Iteration 4:{space 3}log pseudolikelihood = {res:  -467.196}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-366.15194}  
Iteration 1:{space 3}log pseudolikelihood = {res:-326.54689}  
Iteration 2:{space 3}log pseudolikelihood = {res:-326.54671}  
Iteration 3:{space 3}log pseudolikelihood = {res:-326.54671}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -316.3598}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-297.21035}  
Iteration 2:{space 3}log pseudolikelihood = {res:-278.55761}  
Iteration 3:{space 3}log pseudolikelihood = {res:-273.55027}  
Iteration 4:{space 3}log pseudolikelihood = {res:-273.39275}  
Iteration 5:{space 3}log pseudolikelihood = {res:-273.39246}  
Iteration 6:{space 3}log pseudolikelihood = {res:-273.39246}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       131
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     72.39
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-273.39246{txt}{col 49}Pseudo R2{col 67}= {res}    0.1628

{txt}{ralign 81:(Std. Err. adjusted for {res:24} clusters in ID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}   CAT1_SEA_MIL{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
PTS_MISSION_MIL {c |}{col 17}{res}{space 2} .6047373{col 29}{space 2} .3881204{col 40}{space 1}    1.56{col 49}{space 3}0.119{col 57}{space 4}-.1559647{col 70}{space 3} 1.365439
{txt}{space 1}DEMOCRATIC_MIL {c |}{col 17}{res}{space 2} .0581814{col 29}{space 2} .2996068{col 40}{space 1}    0.19{col 49}{space 3}0.846{col 57}{space 4}-.5290372{col 70}{space 3} .6453999
{txt}{space 5}GENDER_MIL {c |}{col 17}{res}{space 2}-9.416303{col 29}{space 2} 8.844997{col 40}{space 1}   -1.06{col 49}{space 3}0.287{col 57}{space 4}-26.75218{col 70}{space 3} 7.919574
{txt}{space 5}FORCE_SIZE {c |}{col 17}{res}{space 2} .0001648{col 29}{space 2}  .000055{col 40}{space 1}    3.00{col 49}{space 3}0.003{col 57}{space 4}  .000057{col 70}{space 3} .0002726
{txt}{space 7}MANDATE3 {c |}{col 17}{res}{space 2} .1110024{col 29}{space 2} .4859167{col 40}{space 1}    0.23{col 49}{space 3}0.819{col 57}{space 4}-.8413768{col 70}{space 3} 1.063382
{txt}{space 7}GDP_HOST {c |}{col 17}{res}{space 2}-5.18e-06{col 29}{space 2}  .000028{col 40}{space 1}   -0.19{col 49}{space 3}0.853{col 57}{space 4}  -.00006{col 70}{space 3} .0000496
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-1.303886{col 29}{space 2} .9098539{col 40}{space 1}   -1.43{col 49}{space 3}0.152{col 57}{space 4}-3.087167{col 70}{space 3} .4793944
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/lnalpha {c |}{col 17}{res}{space 2} .0511424{col 29}{space 2} .2178657{col 57}{space 4}-.3758665{col 70}{space 3} .4781513
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          alpha {c |}{col 17}{res}{space 2} 1.052473{col 29}{space 2} .2292977{col 57}{space 4}  .686694{col 70}{space 3}  1.61309
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 8

. nbreg CAT1_SEA_MIL PTS_MISSION_MIL GDP_MISSION_MIL GENDER_MIL FORCE_SIZE MANDATE3 GDP_HOST, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-476.34307}  
Iteration 1:{space 3}log pseudolikelihood = {res:-464.66157}  
Iteration 2:{space 3}log pseudolikelihood = {res:-464.11322}  
Iteration 3:{space 3}log pseudolikelihood = {res:-464.10935}  
Iteration 4:{space 3}log pseudolikelihood = {res:-464.10935}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-366.15194}  
Iteration 1:{space 3}log pseudolikelihood = {res:-326.54689}  
Iteration 2:{space 3}log pseudolikelihood = {res:-326.54671}  
Iteration 3:{space 3}log pseudolikelihood = {res:-326.54671}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-314.80775}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-294.52965}  
Iteration 2:{space 3}log pseudolikelihood = {res: -276.4954}  
Iteration 3:{space 3}log pseudolikelihood = {res:-272.88094}  
Iteration 4:{space 3}log pseudolikelihood = {res:-272.78749}  
Iteration 5:{space 3}log pseudolikelihood = {res:-272.78742}  
Iteration 6:{space 3}log pseudolikelihood = {res:-272.78742}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       131
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     74.72
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-272.78742{txt}{col 49}Pseudo R2{col 67}= {res}    0.1646

{txt}{ralign 81:(Std. Err. adjusted for {res:24} clusters in ID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}   CAT1_SEA_MIL{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
PTS_MISSION_MIL {c |}{col 17}{res}{space 2} .5262137{col 29}{space 2} .3288265{col 40}{space 1}    1.60{col 49}{space 3}0.110{col 57}{space 4}-.1182743{col 70}{space 3} 1.170702
{txt}GDP_MISSION_MIL {c |}{col 17}{res}{space 2}-.0000162{col 29}{space 2} .0000237{col 40}{space 1}   -0.69{col 49}{space 3}0.493{col 57}{space 4}-.0000626{col 70}{space 3} .0000301
{txt}{space 5}GENDER_MIL {c |}{col 17}{res}{space 2}-10.44539{col 29}{space 2} 8.685102{col 40}{space 1}   -1.20{col 49}{space 3}0.229{col 57}{space 4}-27.46788{col 70}{space 3} 6.577092
{txt}{space 5}FORCE_SIZE {c |}{col 17}{res}{space 2} .0001557{col 29}{space 2} .0000542{col 40}{space 1}    2.87{col 49}{space 3}0.004{col 57}{space 4} .0000495{col 70}{space 3} .0002618
{txt}{space 7}MANDATE3 {c |}{col 17}{res}{space 2} .1049966{col 29}{space 2} .4604417{col 40}{space 1}    0.23{col 49}{space 3}0.820{col 57}{space 4}-.7974526{col 70}{space 3} 1.007446
{txt}{space 7}GDP_HOST {c |}{col 17}{res}{space 2} 8.51e-07{col 29}{space 2} .0000269{col 40}{space 1}    0.03{col 49}{space 3}0.975{col 57}{space 4}-.0000518{col 70}{space 3} .0000535
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}  -.85453{col 29}{space 2} 1.132935{col 40}{space 1}   -0.75{col 49}{space 3}0.451{col 57}{space 4}-3.075041{col 70}{space 3} 1.365981
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/lnalpha {c |}{col 17}{res}{space 2} .0204429{col 29}{space 2} .2354347{col 57}{space 4}-.4410006{col 70}{space 3} .4818864
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          alpha {c |}{col 17}{res}{space 2} 1.020653{col 29}{space 2} .2402972{col 57}{space 4} .6433923{col 70}{space 3} 1.619126
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 9

. nbreg CAT1_SEA_MIL CORRUPTION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_SIZE MANDATE3 GDP_HOST, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-473.78029}  
Iteration 1:{space 3}log pseudolikelihood = {res:-464.96701}  
Iteration 2:{space 3}log pseudolikelihood = {res:-464.50035}  
Iteration 3:{space 3}log pseudolikelihood = {res:-464.49804}  
Iteration 4:{space 3}log pseudolikelihood = {res:-464.49804}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-366.15194}  
Iteration 1:{space 3}log pseudolikelihood = {res:-326.54689}  
Iteration 2:{space 3}log pseudolikelihood = {res:-326.54671}  
Iteration 3:{space 3}log pseudolikelihood = {res:-326.54671}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-310.98443}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:  -291.155}  
Iteration 2:{space 3}log pseudolikelihood = {res:-272.30599}  
Iteration 3:{space 3}log pseudolikelihood = {res: -270.6403}  
Iteration 4:{space 3}log pseudolikelihood = {res:-270.59937}  
Iteration 5:{space 3}log pseudolikelihood = {res:-270.59935}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       131
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     77.78
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-270.59935{txt}{col 49}Pseudo R2{col 67}= {res}    0.1713

{txt}{ralign 80:(Std. Err. adjusted for {res:24} clusters in ID)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}  CAT1_SEA_MIL{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
CORRUPTION_MIL {c |}{col 16}{res}{space 2}-1.100313{col 28}{space 2} .4936667{col 39}{space 1}   -2.23{col 48}{space 3}0.026{col 56}{space 4}-2.067882{col 69}{space 3}-.1327437
{txt}DEMOCRATIC_MIL {c |}{col 16}{res}{space 2}-.2591512{col 28}{space 2} .3690285{col 39}{space 1}   -0.70{col 48}{space 3}0.483{col 56}{space 4}-.9824338{col 69}{space 3} .4641313
{txt}{space 4}GENDER_MIL {c |}{col 16}{res}{space 2}-8.610117{col 28}{space 2} 7.429541{col 39}{space 1}   -1.16{col 48}{space 3}0.246{col 56}{space 4}-23.17175{col 69}{space 3} 5.951516
{txt}{space 4}FORCE_SIZE {c |}{col 16}{res}{space 2} .0001714{col 28}{space 2} .0000478{col 39}{space 1}    3.58{col 48}{space 3}0.000{col 56}{space 4} .0000777{col 69}{space 3} .0002651
{txt}{space 6}MANDATE3 {c |}{col 16}{res}{space 2} .2817643{col 28}{space 2} .3615752{col 39}{space 1}    0.78{col 48}{space 3}0.436{col 56}{space 4}-.4269101{col 69}{space 3} .9904387
{txt}{space 6}GDP_HOST {c |}{col 16}{res}{space 2}-.0000108{col 28}{space 2} .0000286{col 39}{space 1}   -0.38{col 48}{space 3}0.706{col 56}{space 4}-.0000668{col 69}{space 3} .0000453
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}  -.21229{col 28}{space 2} .7234773{col 39}{space 1}   -0.29{col 48}{space 3}0.769{col 56}{space 4}-1.630279{col 69}{space 3} 1.205699
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}/lnalpha {c |}{col 16}{res}{space 2}-.0532129{col 28}{space 2} .2563659{col 56}{space 4}-.5556808{col 69}{space 3} .4492551
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
         alpha {c |}{col 16}{res}{space 2} .9481782{col 28}{space 2} .2430806{col 56}{space 4} .5736816{col 69}{space 3} 1.567144
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. ***CAT1_MIL Models, reported in Table 2

. 
. *Model 10

. nbreg CAT1_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_SIZE MANDATE3 POP_DENSITY, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-342.19011}  
Iteration 1:{space 3}log pseudolikelihood = {res: -336.0538}  
Iteration 2:{space 3}log pseudolikelihood = {res:-336.00928}  
Iteration 3:{space 3}log pseudolikelihood = {res:-336.00926}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-327.36556}  
Iteration 1:{space 3}log pseudolikelihood = {res:-297.94672}  
Iteration 2:{space 3}log pseudolikelihood = {res:-297.83249}  
Iteration 3:{space 3}log pseudolikelihood = {res:-297.83248}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-283.28361}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-263.78033}  
Iteration 2:{space 3}log pseudolikelihood = {res:-249.79907}  
Iteration 3:{space 3}log pseudolikelihood = {res:-246.38322}  
Iteration 4:{space 3}log pseudolikelihood = {res:-246.26963}  
Iteration 5:{space 3}log pseudolikelihood = {res:-246.26951}  
Iteration 6:{space 3}log pseudolikelihood = {res:-246.26951}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     89.02
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-246.26951{txt}{col 49}Pseudo R2{col 67}= {res}    0.1731

{txt}{ralign 81:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}       CAT1_MIL{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
PTS_MISSION_MIL {c |}{col 17}{res}{space 2} .6044617{col 29}{space 2} .2265852{col 40}{space 1}    2.67{col 49}{space 3}0.008{col 57}{space 4} .1603629{col 70}{space 3} 1.048561
{txt}{space 1}DEMOCRATIC_MIL {c |}{col 17}{res}{space 2} .3435443{col 29}{space 2} .2507167{col 40}{space 1}    1.37{col 49}{space 3}0.171{col 57}{space 4}-.1478513{col 70}{space 3} .8349399
{txt}{space 5}GENDER_MIL {c |}{col 17}{res}{space 2}-17.94953{col 29}{space 2} 9.517172{col 40}{space 1}   -1.89{col 49}{space 3}0.059{col 57}{space 4}-36.60284{col 70}{space 3} .7037824
{txt}{space 5}FORCE_SIZE {c |}{col 17}{res}{space 2} .0001261{col 29}{space 2} .0000266{col 40}{space 1}    4.74{col 49}{space 3}0.000{col 57}{space 4} .0000739{col 70}{space 3} .0001782
{txt}{space 7}MANDATE3 {c |}{col 17}{res}{space 2} .0480737{col 29}{space 2} .3455088{col 40}{space 1}    0.14{col 49}{space 3}0.889{col 57}{space 4}-.6291112{col 70}{space 3} .7252585
{txt}{space 4}POP_DENSITY {c |}{col 17}{res}{space 2} .0027141{col 29}{space 2} .0015103{col 40}{space 1}    1.80{col 49}{space 3}0.072{col 57}{space 4}-.0002461{col 70}{space 3} .0056742
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-1.629496{col 29}{space 2} .6677253{col 40}{space 1}   -2.44{col 49}{space 3}0.015{col 57}{space 4}-2.938214{col 70}{space 3}-.3207786
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/lnalpha {c |}{col 17}{res}{space 2}-.0759451{col 29}{space 2} .1834959{col 57}{space 4}-.4355904{col 70}{space 3} .2837002
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          alpha {c |}{col 17}{res}{space 2} .9268671{col 29}{space 2} .1700763{col 57}{space 4} .6468827{col 70}{space 3} 1.328035
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 11

. nbreg CAT1_MIL PTS_MISSION_MIL GDP_MISSION_MIL GENDER_MIL FORCE_SIZE MANDATE3 POP_DENSITY, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-349.47359}  
Iteration 1:{space 3}log pseudolikelihood = {res:-344.41104}  
Iteration 2:{space 3}log pseudolikelihood = {res:-344.36883}  
Iteration 3:{space 3}log pseudolikelihood = {res: -344.3688}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-327.36556}  
Iteration 1:{space 3}log pseudolikelihood = {res:-297.94672}  
Iteration 2:{space 3}log pseudolikelihood = {res:-297.83249}  
Iteration 3:{space 3}log pseudolikelihood = {res:-297.83248}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-281.56994}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res: -262.9024}  
Iteration 2:{space 3}log pseudolikelihood = {res:-250.64775}  
Iteration 3:{space 3}log pseudolikelihood = {res:-247.12454}  
Iteration 4:{space 3}log pseudolikelihood = {res:-246.95899}  
Iteration 5:{space 3}log pseudolikelihood = {res:-246.95863}  
Iteration 6:{space 3}log pseudolikelihood = {res:-246.95863}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     98.51
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-246.95863{txt}{col 49}Pseudo R2{col 67}= {res}    0.1708

{txt}{ralign 81:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}       CAT1_MIL{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
PTS_MISSION_MIL {c |}{col 17}{res}{space 2} .6429817{col 29}{space 2} .2601973{col 40}{space 1}    2.47{col 49}{space 3}0.013{col 57}{space 4} .1330044{col 70}{space 3} 1.152959
{txt}GDP_MISSION_MIL {c |}{col 17}{res}{space 2}-.0000198{col 29}{space 2} .0000255{col 40}{space 1}   -0.78{col 49}{space 3}0.437{col 57}{space 4}-.0000697{col 70}{space 3} .0000301
{txt}{space 5}GENDER_MIL {c |}{col 17}{res}{space 2}-18.32316{col 29}{space 2} 8.897704{col 40}{space 1}   -2.06{col 49}{space 3}0.039{col 57}{space 4}-35.76234{col 70}{space 3}-.8839753
{txt}{space 5}FORCE_SIZE {c |}{col 17}{res}{space 2} .0001211{col 29}{space 2} .0000288{col 40}{space 1}    4.20{col 49}{space 3}0.000{col 57}{space 4} .0000646{col 70}{space 3} .0001775
{txt}{space 7}MANDATE3 {c |}{col 17}{res}{space 2} .0109279{col 29}{space 2} .3362322{col 40}{space 1}    0.03{col 49}{space 3}0.974{col 57}{space 4} -.648075{col 70}{space 3} .6699308
{txt}{space 4}POP_DENSITY {c |}{col 17}{res}{space 2} .0021335{col 29}{space 2} .0013094{col 40}{space 1}    1.63{col 49}{space 3}0.103{col 57}{space 4}-.0004329{col 70}{space 3} .0046999
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} -1.25803{col 29}{space 2} .9874125{col 40}{space 1}   -1.27{col 49}{space 3}0.203{col 57}{space 4}-3.193323{col 70}{space 3} .6772629
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/lnalpha {c |}{col 17}{res}{space 2}-.0630446{col 29}{space 2}   .21296{col 57}{space 4}-.4804385{col 70}{space 3} .3543493
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          alpha {c |}{col 17}{res}{space 2} .9389016{col 29}{space 2} .1999485{col 57}{space 4} .6185121{col 70}{space 3} 1.425253
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 12

. nbreg CAT1_MIL CORRUPTION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_SIZE MANDATE3 POP_DENSITY, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-342.52644}  
Iteration 1:{space 3}log pseudolikelihood = {res:-337.00284}  
Iteration 2:{space 3}log pseudolikelihood = {res:-336.96163}  
Iteration 3:{space 3}log pseudolikelihood = {res:-336.96162}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-327.36556}  
Iteration 1:{space 3}log pseudolikelihood = {res:-297.94672}  
Iteration 2:{space 3}log pseudolikelihood = {res:-297.83249}  
Iteration 3:{space 3}log pseudolikelihood = {res:-297.83248}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-280.77884}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-261.47615}  
Iteration 2:{space 3}log pseudolikelihood = {res: -248.0311}  
Iteration 3:{space 3}log pseudolikelihood = {res:-246.06261}  
Iteration 4:{space 3}log pseudolikelihood = {res:-246.01226}  
Iteration 5:{space 3}log pseudolikelihood = {res:-246.01222}  
Iteration 6:{space 3}log pseudolikelihood = {res:-246.01222}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     92.01
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-246.01222{txt}{col 49}Pseudo R2{col 67}= {res}    0.1740

{txt}{ralign 80:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}      CAT1_MIL{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
CORRUPTION_MIL {c |}{col 16}{res}{space 2}-.8062787{col 28}{space 2} .3500228{col 39}{space 1}   -2.30{col 48}{space 3}0.021{col 56}{space 4}-1.492311{col 69}{space 3}-.1202466
{txt}DEMOCRATIC_MIL {c |}{col 16}{res}{space 2} .2595507{col 28}{space 2} .2532192{col 39}{space 1}    1.03{col 48}{space 3}0.305{col 56}{space 4}-.2367498{col 69}{space 3} .7558512
{txt}{space 4}GENDER_MIL {c |}{col 16}{res}{space 2} -15.0192{col 28}{space 2} 6.886699{col 39}{space 1}   -2.18{col 48}{space 3}0.029{col 56}{space 4}-28.51688{col 69}{space 3}-1.521515
{txt}{space 4}FORCE_SIZE {c |}{col 16}{res}{space 2} .0001344{col 28}{space 2} .0000253{col 39}{space 1}    5.31{col 48}{space 3}0.000{col 56}{space 4} .0000848{col 69}{space 3}  .000184
{txt}{space 6}MANDATE3 {c |}{col 16}{res}{space 2}  .182148{col 28}{space 2} .3093496{col 39}{space 1}    0.59{col 48}{space 3}0.556{col 56}{space 4}-.4241661{col 69}{space 3}  .788462
{txt}{space 3}POP_DENSITY {c |}{col 16}{res}{space 2} .0033808{col 28}{space 2}  .001787{col 39}{space 1}    1.89{col 48}{space 3}0.058{col 56}{space 4}-.0001216{col 69}{space 3} .0068832
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.6659943{col 28}{space 2} .4824908{col 39}{space 1}   -1.38{col 48}{space 3}0.167{col 56}{space 4}-1.611659{col 69}{space 3} .2796704
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}/lnalpha {c |}{col 16}{res}{space 2}-.1153854{col 28}{space 2} .2340036{col 56}{space 4}-.5740241{col 69}{space 3} .3432532
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
         alpha {c |}{col 16}{res}{space 2} .8910226{col 28}{space 2} .2085025{col 56}{space 4} .5632543{col 69}{space 3} 1.409526
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 13

. nbreg CAT1_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1200.5037}  
Iteration 1:{space 3}log pseudolikelihood = {res: -616.9813}  
Iteration 2:{space 3}log pseudolikelihood = {res:-365.48047}  
Iteration 3:{space 3}log pseudolikelihood = {res:-347.52114}  
Iteration 4:{space 3}log pseudolikelihood = {res:-347.11443}  
Iteration 5:{space 3}log pseudolikelihood = {res:-347.11417}  
Iteration 6:{space 3}log pseudolikelihood = {res:-347.11417}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-327.36556}  
Iteration 1:{space 3}log pseudolikelihood = {res:-297.94672}  
Iteration 2:{space 3}log pseudolikelihood = {res:-297.83249}  
Iteration 3:{space 3}log pseudolikelihood = {res:-297.83248}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-283.05687}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res: -265.0878}  
Iteration 2:{space 3}log pseudolikelihood = {res: -260.9361}  
Iteration 3:{space 3}log pseudolikelihood = {res:-254.66588}  
Iteration 4:{space 3}log pseudolikelihood = {res:-254.45491}  
Iteration 5:{space 3}log pseudolikelihood = {res:-254.45448}  
Iteration 6:{space 3}log pseudolikelihood = {res:-254.45448}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}    108.24
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-254.45448{txt}{col 49}Pseudo R2{col 67}= {res}    0.1456

{txt}{ralign 86:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}            CAT1_MIL{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}PTS_MISSION_MIL {c |}{col 22}{res}{space 2} .9308142{col 34}{space 2} .2664565{col 45}{space 1}    3.49{col 54}{space 3}0.000{col 62}{space 4} .4085689{col 75}{space 3} 1.453059
{txt}{space 6}DEMOCRATIC_MIL {c |}{col 22}{res}{space 2} .1330209{col 34}{space 2} .2156443{col 45}{space 1}    0.62{col 54}{space 3}0.537{col 62}{space 4}-.2896341{col 75}{space 3} .5556759
{txt}{space 10}GENDER_MIL {c |}{col 22}{res}{space 2}-20.97347{col 34}{space 2} 8.029033{col 45}{space 1}   -2.61{col 54}{space 3}0.009{col 62}{space 4}-36.71009{col 75}{space 3}-5.236857
{txt}{space 7}FORCE_DENSITY {c |}{col 22}{res}{space 2} 1.789702{col 34}{space 2} .5630486{col 45}{space 1}    3.18{col 54}{space 3}0.001{col 62}{space 4} .6861467{col 75}{space 3} 2.893257
{txt}PKO_FATALITIES_TOTAL {c |}{col 22}{res}{space 2} .0430086{col 34}{space 2} .0250464{col 45}{space 1}    1.72{col 54}{space 3}0.086{col 62}{space 4}-.0060814{col 75}{space 3} .0920986
{txt}{space 12}MANDATE3 {c |}{col 22}{res}{space 2} .3068129{col 34}{space 2} .3860842{col 45}{space 1}    0.79{col 54}{space 3}0.427{col 62}{space 4}-.4498981{col 75}{space 3} 1.063524
{txt}{space 6}ln_KM2_country {c |}{col 22}{res}{space 2} .0775027{col 34}{space 2} .1110071{col 45}{space 1}    0.70{col 54}{space 3}0.485{col 62}{space 4}-.1400672{col 75}{space 3} .2950725
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-2.547963{col 34}{space 2} 1.408236{col 45}{space 1}   -1.81{col 54}{space 3}0.070{col 62}{space 4}-5.308054{col 75}{space 3} .2121283
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/lnalpha {c |}{col 22}{res}{space 2} .1066773{col 34}{space 2} .1982254{col 62}{space 4}-.2818373{col 75}{space 3} .4951919
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} 1.112575{col 34}{space 2} .2205406{col 62}{space 4} .7543964{col 75}{space 3} 1.640813
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 14

. nbreg CAT1_MIL PTS_MISSION_MIL GDP_MISSION_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1203.2994}  
Iteration 1:{space 3}log pseudolikelihood = {res:-611.56632}  
Iteration 2:{space 3}log pseudolikelihood = {res:-363.98118}  
Iteration 3:{space 3}log pseudolikelihood = {res:-346.59996}  
Iteration 4:{space 3}log pseudolikelihood = {res:-346.18719}  
Iteration 5:{space 3}log pseudolikelihood = {res:-346.18695}  
Iteration 6:{space 3}log pseudolikelihood = {res:-346.18695}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-327.36556}  
Iteration 1:{space 3}log pseudolikelihood = {res:-297.94672}  
Iteration 2:{space 3}log pseudolikelihood = {res:-297.83249}  
Iteration 3:{space 3}log pseudolikelihood = {res:-297.83248}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-285.02268}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-266.07315}  
Iteration 2:{space 3}log pseudolikelihood = {res:-254.78497}  
Iteration 3:{space 3}log pseudolikelihood = {res:-253.78808}  
Iteration 4:{space 3}log pseudolikelihood = {res:-253.77704}  
Iteration 5:{space 3}log pseudolikelihood = {res:-253.77703}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     98.40
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-253.77703{txt}{col 49}Pseudo R2{col 67}= {res}    0.1479

{txt}{ralign 86:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}            CAT1_MIL{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}PTS_MISSION_MIL {c |}{col 22}{res}{space 2} .8105443{col 34}{space 2} .3099405{col 45}{space 1}    2.62{col 54}{space 3}0.009{col 62}{space 4} .2030721{col 75}{space 3} 1.418016
{txt}{space 5}GDP_MISSION_MIL {c |}{col 22}{res}{space 2}-.0000214{col 34}{space 2} .0000284{col 45}{space 1}   -0.75{col 54}{space 3}0.452{col 62}{space 4}-.0000771{col 75}{space 3} .0000343
{txt}{space 10}GENDER_MIL {c |}{col 22}{res}{space 2}-21.07828{col 34}{space 2} 7.478395{col 45}{space 1}   -2.82{col 54}{space 3}0.005{col 62}{space 4}-35.73567{col 75}{space 3}-6.420897
{txt}{space 7}FORCE_DENSITY {c |}{col 22}{res}{space 2} 1.653126{col 34}{space 2} .5090338{col 45}{space 1}    3.25{col 54}{space 3}0.001{col 62}{space 4} .6554379{col 75}{space 3} 2.650814
{txt}PKO_FATALITIES_TOTAL {c |}{col 22}{res}{space 2} .0412633{col 34}{space 2}  .024992{col 45}{space 1}    1.65{col 54}{space 3}0.099{col 62}{space 4}-.0077201{col 75}{space 3} .0902468
{txt}{space 12}MANDATE3 {c |}{col 22}{res}{space 2} .2717594{col 34}{space 2} .3619346{col 45}{space 1}    0.75{col 54}{space 3}0.453{col 62}{space 4}-.4376193{col 75}{space 3} .9811381
{txt}{space 6}ln_KM2_country {c |}{col 22}{res}{space 2} .0545058{col 34}{space 2} .0961615{col 45}{space 1}    0.57{col 54}{space 3}0.571{col 62}{space 4}-.1339672{col 75}{space 3} .2429789
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-1.679747{col 34}{space 2} 1.529881{col 45}{space 1}   -1.10{col 54}{space 3}0.272{col 62}{space 4}-4.678258{col 75}{space 3} 1.318765
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/lnalpha {c |}{col 22}{res}{space 2} .0611337{col 34}{space 2} .2283069{col 62}{space 4}-.3863396{col 75}{space 3}  .508607
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} 1.063041{col 34}{space 2} .2426996{col 62}{space 4} .6795397{col 75}{space 3} 1.662973
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 15

. nbreg CAT1_MIL CORRUPTION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1233.7137}  
Iteration 1:{space 3}log pseudolikelihood = {res:-713.82163}  
Iteration 2:{space 3}log pseudolikelihood = {res:-387.70061}  
Iteration 3:{space 3}log pseudolikelihood = {res:-363.65952}  
Iteration 4:{space 3}log pseudolikelihood = {res:-363.10127}  
Iteration 5:{space 3}log pseudolikelihood = {res:-363.10029}  
Iteration 6:{space 3}log pseudolikelihood = {res:-363.10029}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-327.36556}  
Iteration 1:{space 3}log pseudolikelihood = {res:-297.94672}  
Iteration 2:{space 3}log pseudolikelihood = {res:-297.83249}  
Iteration 3:{space 3}log pseudolikelihood = {res:-297.83248}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-281.71143}  
Iteration 1:{space 3}log pseudolikelihood = {res:-267.40363}  
Iteration 2:{space 3}log pseudolikelihood = {res: -259.1022}  
Iteration 3:{space 3}log pseudolikelihood = {res:  -258.023}  
Iteration 4:{space 3}log pseudolikelihood = {res:-258.01839}  
Iteration 5:{space 3}log pseudolikelihood = {res:-258.01839}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}    115.67
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-258.01839{txt}{col 49}Pseudo R2{col 67}= {res}    0.1337

{txt}{ralign 86:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}            CAT1_MIL{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}CORRUPTION_MIL {c |}{col 22}{res}{space 2}-.8718004{col 34}{space 2}  .331849{col 45}{space 1}   -2.63{col 54}{space 3}0.009{col 62}{space 4}-1.522212{col 75}{space 3}-.2213884
{txt}{space 6}DEMOCRATIC_MIL {c |}{col 22}{res}{space 2} .0897158{col 34}{space 2} .2661629{col 45}{space 1}    0.34{col 54}{space 3}0.736{col 62}{space 4}-.4319538{col 75}{space 3} .6113855
{txt}{space 10}GENDER_MIL {c |}{col 22}{res}{space 2}-17.53863{col 34}{space 2} 5.592892{col 45}{space 1}   -3.14{col 54}{space 3}0.002{col 62}{space 4}-28.50049{col 75}{space 3}-6.576761
{txt}{space 7}FORCE_DENSITY {c |}{col 22}{res}{space 2} 1.926642{col 34}{space 2} .6793794{col 45}{space 1}    2.84{col 54}{space 3}0.005{col 62}{space 4} .5950829{col 75}{space 3} 3.258201
{txt}PKO_FATALITIES_TOTAL {c |}{col 22}{res}{space 2} .0501873{col 34}{space 2} .0296515{col 45}{space 1}    1.69{col 54}{space 3}0.091{col 62}{space 4}-.0079286{col 75}{space 3} .1083032
{txt}{space 12}MANDATE3 {c |}{col 22}{res}{space 2} .5515011{col 34}{space 2} .4091364{col 45}{space 1}    1.35{col 54}{space 3}0.178{col 62}{space 4}-.2503915{col 75}{space 3} 1.353394
{txt}{space 6}ln_KM2_country {c |}{col 22}{res}{space 2} .0693109{col 34}{space 2} .1222033{col 45}{space 1}    0.57{col 54}{space 3}0.571{col 62}{space 4}-.1702032{col 75}{space 3}  .308825
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-.7485487{col 34}{space 2} 1.545768{col 45}{space 1}   -0.48{col 54}{space 3}0.628{col 62}{space 4}-3.778199{col 75}{space 3} 2.281101
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/lnalpha {c |}{col 22}{res}{space 2} .1829067{col 34}{space 2} .2757897{col 62}{space 4}-.3576313{col 75}{space 3} .7234446
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} 1.200702{col 34}{space 2} .3311414{col 62}{space 4} .6993309{col 75}{space 3} 2.061522
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 16

. nbreg CAT1_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_SIZE MANDATE3 GDP_HOST, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-341.73046}  
Iteration 1:{space 3}log pseudolikelihood = {res:-338.37676}  
Iteration 2:{space 3}log pseudolikelihood = {res:-338.34723}  
Iteration 3:{space 3}log pseudolikelihood = {res:-338.34718}  
Iteration 4:{space 3}log pseudolikelihood = {res:-338.34718}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-296.94495}  
Iteration 1:{space 3}log pseudolikelihood = {res:-272.56082}  
Iteration 2:{space 3}log pseudolikelihood = {res:-272.52793}  
Iteration 3:{space 3}log pseudolikelihood = {res:-272.52793}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-256.74895}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-241.36695}  
Iteration 2:{space 3}log pseudolikelihood = {res:-229.70342}  
Iteration 3:{space 3}log pseudolikelihood = {res:  -228.825}  
Iteration 4:{space 3}log pseudolikelihood = {res: -228.8181}  
Iteration 5:{space 3}log pseudolikelihood = {res: -228.8181}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       131
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}    107.79
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res} -228.8181{txt}{col 49}Pseudo R2{col 67}= {res}    0.1604

{txt}{ralign 81:(Std. Err. adjusted for {res:24} clusters in ID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}       CAT1_MIL{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
PTS_MISSION_MIL {c |}{col 17}{res}{space 2} .6420122{col 29}{space 2} .2860823{col 40}{space 1}    2.24{col 49}{space 3}0.025{col 57}{space 4} .0813013{col 70}{space 3} 1.202723
{txt}{space 1}DEMOCRATIC_MIL {c |}{col 17}{res}{space 2}  .030831{col 29}{space 2} .2587002{col 40}{space 1}    0.12{col 49}{space 3}0.905{col 57}{space 4}-.4762121{col 70}{space 3} .5378741
{txt}{space 5}GENDER_MIL {c |}{col 17}{res}{space 2}-15.47283{col 29}{space 2} 8.545484{col 40}{space 1}   -1.81{col 49}{space 3}0.070{col 57}{space 4}-32.22168{col 70}{space 3} 1.276007
{txt}{space 5}FORCE_SIZE {c |}{col 17}{res}{space 2} .0001359{col 29}{space 2} .0000433{col 40}{space 1}    3.14{col 49}{space 3}0.002{col 57}{space 4}  .000051{col 70}{space 3} .0002208
{txt}{space 7}MANDATE3 {c |}{col 17}{res}{space 2} .0478356{col 29}{space 2} .3943803{col 40}{space 1}    0.12{col 49}{space 3}0.903{col 57}{space 4}-.7251356{col 70}{space 3} .8208069
{txt}{space 7}GDP_HOST {c |}{col 17}{res}{space 2}-4.27e-06{col 29}{space 2} .0000226{col 40}{space 1}   -0.19{col 49}{space 3}0.850{col 57}{space 4}-.0000485{col 70}{space 3}   .00004
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-1.379842{col 29}{space 2} .8779543{col 40}{space 1}   -1.57{col 49}{space 3}0.116{col 57}{space 4}-3.100601{col 70}{space 3}  .340917
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/lnalpha {c |}{col 17}{res}{space 2}  .034248{col 29}{space 2} .1985674{col 57}{space 4} -.354937{col 70}{space 3} .4234331
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          alpha {c |}{col 17}{res}{space 2} 1.034841{col 29}{space 2} .2054858{col 57}{space 4} .7012176{col 70}{space 3} 1.527196
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 17

. nbreg CAT1_MIL PTS_MISSION_MIL GDP_MISSION_MIL GENDER_MIL FORCE_SIZE MANDATE3 GDP_HOST, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-342.62652}  
Iteration 1:{space 3}log pseudolikelihood = {res:-339.57847}  
Iteration 2:{space 3}log pseudolikelihood = {res:-339.55206}  
Iteration 3:{space 3}log pseudolikelihood = {res:-339.55202}  
Iteration 4:{space 3}log pseudolikelihood = {res:-339.55202}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-296.94495}  
Iteration 1:{space 3}log pseudolikelihood = {res:-272.56082}  
Iteration 2:{space 3}log pseudolikelihood = {res:-272.52793}  
Iteration 3:{space 3}log pseudolikelihood = {res:-272.52793}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-256.05131}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-240.29932}  
Iteration 2:{space 3}log pseudolikelihood = {res:-229.82043}  
Iteration 3:{space 3}log pseudolikelihood = {res:-228.80311}  
Iteration 4:{space 3}log pseudolikelihood = {res:-228.79226}  
Iteration 5:{space 3}log pseudolikelihood = {res:-228.79226}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       131
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}    108.89
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-228.79226{txt}{col 49}Pseudo R2{col 67}= {res}    0.1605

{txt}{ralign 81:(Std. Err. adjusted for {res:24} clusters in ID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}       CAT1_MIL{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
PTS_MISSION_MIL {c |}{col 17}{res}{space 2}  .629353{col 29}{space 2} .2639302{col 40}{space 1}    2.38{col 49}{space 3}0.017{col 57}{space 4} .1120593{col 70}{space 3} 1.146647
{txt}GDP_MISSION_MIL {c |}{col 17}{res}{space 2}-4.23e-06{col 29}{space 2} .0000184{col 40}{space 1}   -0.23{col 49}{space 3}0.818{col 57}{space 4}-.0000402{col 70}{space 3} .0000318
{txt}{space 5}GENDER_MIL {c |}{col 17}{res}{space 2}-15.73812{col 29}{space 2} 8.562874{col 40}{space 1}   -1.84{col 49}{space 3}0.066{col 57}{space 4}-32.52105{col 70}{space 3} 1.044804
{txt}{space 5}FORCE_SIZE {c |}{col 17}{res}{space 2} .0001337{col 29}{space 2} .0000452{col 40}{space 1}    2.96{col 49}{space 3}0.003{col 57}{space 4}  .000045{col 70}{space 3} .0002223
{txt}{space 7}MANDATE3 {c |}{col 17}{res}{space 2} .0403995{col 29}{space 2} .4003338{col 40}{space 1}    0.10{col 49}{space 3}0.920{col 57}{space 4}-.7442403{col 70}{space 3} .8250393
{txt}{space 7}GDP_HOST {c |}{col 17}{res}{space 2}-3.18e-06{col 29}{space 2} .0000214{col 40}{space 1}   -0.15{col 49}{space 3}0.882{col 57}{space 4}-.0000451{col 70}{space 3} .0000388
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-1.269156{col 29}{space 2} 1.099862{col 40}{space 1}   -1.15{col 49}{space 3}0.249{col 57}{space 4}-3.424846{col 70}{space 3} .8865333
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/lnalpha {c |}{col 17}{res}{space 2} .0290779{col 29}{space 2} .2089088{col 57}{space 4}-.3803758{col 70}{space 3} .4385317
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          alpha {c |}{col 17}{res}{space 2} 1.029505{col 29}{space 2} .2150726{col 57}{space 4} .6836044{col 70}{space 3} 1.550429
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 18

. nbreg CAT1_MIL CORRUPTION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_SIZE MANDATE3 GDP_HOST, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-348.50849}  
Iteration 1:{space 3}log pseudolikelihood = {res:-345.83282}  
Iteration 2:{space 3}log pseudolikelihood = {res:-345.78612}  
Iteration 3:{space 3}log pseudolikelihood = {res:-345.78598}  
Iteration 4:{space 3}log pseudolikelihood = {res:-345.78598}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-296.94495}  
Iteration 1:{space 3}log pseudolikelihood = {res:-272.56082}  
Iteration 2:{space 3}log pseudolikelihood = {res:-272.52793}  
Iteration 3:{space 3}log pseudolikelihood = {res:-272.52793}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-255.72078}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-240.89401}  
Iteration 2:{space 3}log pseudolikelihood = {res:-230.10705}  
Iteration 3:{space 3}log pseudolikelihood = {res: -229.8751}  
Iteration 4:{space 3}log pseudolikelihood = {res:-229.87452}  
Iteration 5:{space 3}log pseudolikelihood = {res:-229.87452}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       131
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     78.80
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-229.87452{txt}{col 49}Pseudo R2{col 67}= {res}    0.1565

{txt}{ralign 80:(Std. Err. adjusted for {res:24} clusters in ID)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}      CAT1_MIL{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
CORRUPTION_MIL {c |}{col 16}{res}{space 2}-.6381648{col 28}{space 2} .4116117{col 39}{space 1}   -1.55{col 48}{space 3}0.121{col 56}{space 4}-1.444909{col 69}{space 3} .1685792
{txt}DEMOCRATIC_MIL {c |}{col 16}{res}{space 2}-.0549943{col 28}{space 2} .3201514{col 39}{space 1}   -0.17{col 48}{space 3}0.864{col 56}{space 4}-.6824795{col 69}{space 3} .5724909
{txt}{space 4}GENDER_MIL {c |}{col 16}{res}{space 2}  -12.014{col 28}{space 2} 6.495165{col 39}{space 1}   -1.85{col 48}{space 3}0.064{col 56}{space 4}-24.74429{col 69}{space 3} .7162943
{txt}{space 4}FORCE_SIZE {c |}{col 16}{res}{space 2} .0001527{col 28}{space 2} .0000468{col 39}{space 1}    3.26{col 48}{space 3}0.001{col 56}{space 4}  .000061{col 69}{space 3} .0002444
{txt}{space 6}MANDATE3 {c |}{col 16}{res}{space 2}  .232003{col 28}{space 2} .3423757{col 39}{space 1}    0.68{col 48}{space 3}0.498{col 56}{space 4}-.4390411{col 69}{space 3}  .903047
{txt}{space 6}GDP_HOST {c |}{col 16}{res}{space 2}-.0000104{col 28}{space 2} .0000255{col 39}{space 1}   -0.41{col 48}{space 3}0.683{col 56}{space 4}-.0000605{col 69}{space 3} .0000396
{txt}{space 9}_cons {c |}{col 16}{res}{space 2}-.3293685{col 28}{space 2} .6817451{col 39}{space 1}   -0.48{col 48}{space 3}0.629{col 56}{space 4}-1.665564{col 69}{space 3} 1.006827
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}/lnalpha {c |}{col 16}{res}{space 2} .0577244{col 28}{space 2} .2415095{col 56}{space 4}-.4156254{col 69}{space 3} .5310743
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
         alpha {c |}{col 16}{res}{space 2} 1.059423{col 28}{space 2} .2558607{col 56}{space 4} .6599274{col 69}{space 3} 1.700758
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. 
. ***CAT2_MIL Models, reported in Table 3

. 
. *Model 19

. nbreg CAT2_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_SIZE MANDATE3 POP_DENSITY, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-727.81648}  
Iteration 1:{space 3}log pseudolikelihood = {res:-712.09899}  
Iteration 2:{space 3}log pseudolikelihood = {res:-712.08558}  
Iteration 3:{space 3}log pseudolikelihood = {res:-712.08558}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-486.60088}  
Iteration 1:{space 3}log pseudolikelihood = {res:-430.34183}  
Iteration 2:{space 3}log pseudolikelihood = {res:-429.96601}  
Iteration 3:{space 3}log pseudolikelihood = {res:-429.96574}  
Iteration 4:{space 3}log pseudolikelihood = {res:-429.96574}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-408.09028}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-388.52487}  
Iteration 2:{space 3}log pseudolikelihood = {res:-383.59609}  
Iteration 3:{space 3}log pseudolikelihood = {res:-375.07355}  
Iteration 4:{space 3}log pseudolikelihood = {res:-374.92081}  
Iteration 5:{space 3}log pseudolikelihood = {res:-374.92073}  
Iteration 6:{space 3}log pseudolikelihood = {res:-374.92073}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     89.06
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-374.92073{txt}{col 49}Pseudo R2{col 67}= {res}    0.1280

{txt}{ralign 81:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}       CAT2_MIL{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
PTS_MISSION_MIL {c |}{col 17}{res}{space 2} .9151929{col 29}{space 2} .1988892{col 40}{space 1}    4.60{col 49}{space 3}0.000{col 57}{space 4} .5253773{col 70}{space 3} 1.305009
{txt}{space 1}DEMOCRATIC_MIL {c |}{col 17}{res}{space 2}-.2773308{col 29}{space 2} .3826495{col 40}{space 1}   -0.72{col 49}{space 3}0.469{col 57}{space 4} -1.02731{col 70}{space 3} .4726485
{txt}{space 5}GENDER_MIL {c |}{col 17}{res}{space 2}-11.30668{col 29}{space 2} 6.242064{col 40}{space 1}   -1.81{col 49}{space 3}0.070{col 57}{space 4}-23.54091{col 70}{space 3} .9275361
{txt}{space 5}FORCE_SIZE {c |}{col 17}{res}{space 2} .0001754{col 29}{space 2} .0000327{col 40}{space 1}    5.37{col 49}{space 3}0.000{col 57}{space 4} .0001113{col 70}{space 3} .0002394
{txt}{space 7}MANDATE3 {c |}{col 17}{res}{space 2}-.8857734{col 29}{space 2} .3752899{col 40}{space 1}   -2.36{col 49}{space 3}0.018{col 57}{space 4}-1.621328{col 70}{space 3}-.1502187
{txt}{space 4}POP_DENSITY {c |}{col 17}{res}{space 2}-.0005603{col 29}{space 2} .0011991{col 40}{space 1}   -0.47{col 49}{space 3}0.640{col 57}{space 4}-.0029104{col 70}{space 3} .0017898
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}  -.65486{col 29}{space 2} .4502064{col 40}{space 1}   -1.45{col 49}{space 3}0.146{col 57}{space 4}-1.537248{col 70}{space 3} .2275284
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/lnalpha {c |}{col 17}{res}{space 2} .2346334{col 29}{space 2} .2563993{col 57}{space 4}   -.2679{col 70}{space 3} .7371668
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          alpha {c |}{col 17}{res}{space 2} 1.264445{col 29}{space 2} .3242029{col 57}{space 4} .7649842{col 70}{space 3} 2.090006
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 20

. nbreg CAT2_MIL PTS_MISSION_MIL GDP_MISSION_MIL GENDER_MIL FORCE_SIZE MANDATE3 POP_DENSITY, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-712.61697}  
Iteration 1:{space 3}log pseudolikelihood = {res:-689.56923}  
Iteration 2:{space 3}log pseudolikelihood = {res:-689.53676}  
Iteration 3:{space 3}log pseudolikelihood = {res:-689.53676}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-486.60088}  
Iteration 1:{space 3}log pseudolikelihood = {res:-430.34183}  
Iteration 2:{space 3}log pseudolikelihood = {res:-429.96601}  
Iteration 3:{space 3}log pseudolikelihood = {res:-429.96574}  
Iteration 4:{space 3}log pseudolikelihood = {res:-429.96574}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-406.85758}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-382.77579}  
Iteration 2:{space 3}log pseudolikelihood = {res:-376.22103}  
Iteration 3:{space 3}log pseudolikelihood = {res: -371.6682}  
Iteration 4:{space 3}log pseudolikelihood = {res:-371.54624}  
Iteration 5:{space 3}log pseudolikelihood = {res:-371.54599}  
Iteration 6:{space 3}log pseudolikelihood = {res:-371.54599}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     98.30
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-371.54599{txt}{col 49}Pseudo R2{col 67}= {res}    0.1359

{txt}{ralign 81:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}       CAT2_MIL{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
PTS_MISSION_MIL {c |}{col 17}{res}{space 2} .3794223{col 29}{space 2} .2705395{col 40}{space 1}    1.40{col 49}{space 3}0.161{col 57}{space 4}-.1508255{col 70}{space 3}   .90967
{txt}GDP_MISSION_MIL {c |}{col 17}{res}{space 2}-.0000478{col 29}{space 2}  .000017{col 40}{space 1}   -2.81{col 49}{space 3}0.005{col 57}{space 4}-.0000812{col 70}{space 3}-.0000144
{txt}{space 5}GENDER_MIL {c |}{col 17}{res}{space 2}-10.95378{col 29}{space 2} 5.838158{col 40}{space 1}   -1.88{col 49}{space 3}0.061{col 57}{space 4}-22.39636{col 70}{space 3}  .488794
{txt}{space 5}FORCE_SIZE {c |}{col 17}{res}{space 2} .0001472{col 29}{space 2} .0000278{col 40}{space 1}    5.29{col 49}{space 3}0.000{col 57}{space 4} .0000927{col 70}{space 3} .0002017
{txt}{space 7}MANDATE3 {c |}{col 17}{res}{space 2}-1.043529{col 29}{space 2} .3701709{col 40}{space 1}   -2.82{col 49}{space 3}0.005{col 57}{space 4} -1.76905{col 70}{space 3}-.3180071
{txt}{space 4}POP_DENSITY {c |}{col 17}{res}{space 2} .0010546{col 29}{space 2} .0009138{col 40}{space 1}    1.15{col 49}{space 3}0.248{col 57}{space 4}-.0007365{col 70}{space 3} .0028457
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} 1.110788{col 29}{space 2} .6363763{col 40}{space 1}    1.75{col 49}{space 3}0.081{col 57}{space 4}-.1364871{col 70}{space 3} 2.358062
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/lnalpha {c |}{col 17}{res}{space 2} .1951879{col 29}{space 2}  .278656{col 57}{space 4}-.3509679{col 70}{space 3} .7413436
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          alpha {c |}{col 17}{res}{space 2} 1.215539{col 29}{space 2} .3387173{col 57}{space 4} .7040064{col 70}{space 3} 2.098753
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 21

. nbreg CAT2_MIL CORRUPTION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_SIZE MANDATE3 POP_DENSITY, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-688.63168}  
Iteration 1:{space 3}log pseudolikelihood = {res:-674.34885}  
Iteration 2:{space 3}log pseudolikelihood = {res:-674.33551}  
Iteration 3:{space 3}log pseudolikelihood = {res:-674.33551}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-486.60088}  
Iteration 1:{space 3}log pseudolikelihood = {res:-430.34183}  
Iteration 2:{space 3}log pseudolikelihood = {res:-429.96601}  
Iteration 3:{space 3}log pseudolikelihood = {res:-429.96574}  
Iteration 4:{space 3}log pseudolikelihood = {res:-429.96574}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-405.55216}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-380.35383}  
Iteration 2:{space 3}log pseudolikelihood = {res:-374.75055}  
Iteration 3:{space 3}log pseudolikelihood = {res:-367.59844}  
Iteration 4:{space 3}log pseudolikelihood = {res:-367.25735}  
Iteration 5:{space 3}log pseudolikelihood = {res:-367.25678}  
Iteration 6:{space 3}log pseudolikelihood = {res:-367.25678}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}    138.03
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-367.25678{txt}{col 49}Pseudo R2{col 67}= {res}    0.1458

{txt}{ralign 80:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}      CAT2_MIL{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
CORRUPTION_MIL {c |}{col 16}{res}{space 2}-1.515957{col 28}{space 2} .2524149{col 39}{space 1}   -6.01{col 48}{space 3}0.000{col 56}{space 4}-2.010681{col 69}{space 3}-1.021232
{txt}DEMOCRATIC_MIL {c |}{col 16}{res}{space 2}-.5071778{col 28}{space 2} .3035496{col 39}{space 1}   -1.67{col 48}{space 3}0.095{col 56}{space 4}-1.102124{col 69}{space 3} .0877685
{txt}{space 4}GENDER_MIL {c |}{col 16}{res}{space 2}-12.13569{col 28}{space 2} 4.742315{col 39}{space 1}   -2.56{col 48}{space 3}0.010{col 56}{space 4}-21.43046{col 69}{space 3}-2.840924
{txt}{space 4}FORCE_SIZE {c |}{col 16}{res}{space 2} .0001828{col 28}{space 2} .0000257{col 39}{space 1}    7.10{col 48}{space 3}0.000{col 56}{space 4} .0001323{col 69}{space 3} .0002332
{txt}{space 6}MANDATE3 {c |}{col 16}{res}{space 2}-.7970593{col 28}{space 2} .3709653{col 39}{space 1}   -2.15{col 48}{space 3}0.032{col 56}{space 4}-1.524138{col 69}{space 3}-.0699807
{txt}{space 3}POP_DENSITY {c |}{col 16}{res}{space 2}  .000561{col 28}{space 2}  .001179{col 39}{space 1}    0.48{col 48}{space 3}0.634{col 56}{space 4}-.0017498{col 69}{space 3} .0028718
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} 1.037415{col 28}{space 2} .3734134{col 39}{space 1}    2.78{col 48}{space 3}0.005{col 56}{space 4} .3055382{col 69}{space 3} 1.769292
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}/lnalpha {c |}{col 16}{res}{space 2} .0995729{col 28}{space 2}  .262976{col 56}{space 4}-.4158507{col 69}{space 3} .6149965
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
         alpha {c |}{col 16}{res}{space 2} 1.104699{col 28}{space 2} .2905094{col 56}{space 4} .6597788{col 69}{space 3}  1.84965
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 22

. nbreg CAT2_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1065.2942}  
Iteration 1:{space 3}log pseudolikelihood = {res: -886.5337}  
Iteration 2:{space 3}log pseudolikelihood = {res:-881.86508}  
Iteration 3:{space 3}log pseudolikelihood = {res:-881.82744}  
Iteration 4:{space 3}log pseudolikelihood = {res:-881.82744}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-486.60088}  
Iteration 1:{space 3}log pseudolikelihood = {res:-430.34183}  
Iteration 2:{space 3}log pseudolikelihood = {res:-429.96601}  
Iteration 3:{space 3}log pseudolikelihood = {res:-429.96574}  
Iteration 4:{space 3}log pseudolikelihood = {res:-429.96574}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-414.85108}  
Iteration 1:{space 3}log pseudolikelihood = {res:-407.02379}  
Iteration 2:{space 3}log pseudolikelihood = {res:-390.30486}  
Iteration 3:{space 3}log pseudolikelihood = {res:-387.85342}  
Iteration 4:{space 3}log pseudolikelihood = {res:-387.82853}  
Iteration 5:{space 3}log pseudolikelihood = {res:-387.82852}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     74.16
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-387.82852{txt}{col 49}Pseudo R2{col 67}= {res}    0.0980

{txt}{ralign 86:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}            CAT2_MIL{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}PTS_MISSION_MIL {c |}{col 22}{res}{space 2} 1.411333{col 34}{space 2} .2286318{col 45}{space 1}    6.17{col 54}{space 3}0.000{col 62}{space 4} .9632231{col 75}{space 3} 1.859443
{txt}{space 6}DEMOCRATIC_MIL {c |}{col 22}{res}{space 2} -.209165{col 34}{space 2} .4052607{col 45}{space 1}   -0.52{col 54}{space 3}0.606{col 62}{space 4}-1.003461{col 75}{space 3} .5851314
{txt}{space 10}GENDER_MIL {c |}{col 22}{res}{space 2} -15.4664{col 34}{space 2} 6.611422{col 45}{space 1}   -2.34{col 54}{space 3}0.019{col 62}{space 4}-28.42455{col 75}{space 3}-2.508253
{txt}{space 7}FORCE_DENSITY {c |}{col 22}{res}{space 2} 1.181652{col 34}{space 2} .5966023{col 45}{space 1}    1.98{col 54}{space 3}0.048{col 62}{space 4} .0123326{col 75}{space 3} 2.350971
{txt}PKO_FATALITIES_TOTAL {c |}{col 22}{res}{space 2} .0862756{col 34}{space 2}  .043194{col 45}{space 1}    2.00{col 54}{space 3}0.046{col 62}{space 4}  .001617{col 75}{space 3} .1709342
{txt}{space 12}MANDATE3 {c |}{col 22}{res}{space 2}-.7774369{col 34}{space 2} .5110782{col 45}{space 1}   -1.52{col 54}{space 3}0.128{col 62}{space 4}-1.779132{col 75}{space 3} .2242579
{txt}{space 6}ln_KM2_country {c |}{col 22}{res}{space 2}-.0103356{col 34}{space 2} .1804494{col 45}{space 1}   -0.06{col 54}{space 3}0.954{col 62}{space 4}-.3640099{col 75}{space 3} .3433387
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-1.055273{col 34}{space 2} 2.218271{col 45}{space 1}   -0.48{col 54}{space 3}0.634{col 62}{space 4}-5.403004{col 75}{space 3} 3.292459
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/lnalpha {c |}{col 22}{res}{space 2} .5267624{col 34}{space 2} .2239695{col 62}{space 4} .0877902{col 75}{space 3} .9657346
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} 1.693441{col 34}{space 2} .3792791{col 62}{space 4} 1.091759{col 75}{space 3} 2.626716
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 23

. nbreg CAT2_MIL PTS_MISSION_MIL GDP_MISSION_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-995.18761}  
Iteration 1:{space 3}log pseudolikelihood = {res: -843.4283}  
Iteration 2:{space 3}log pseudolikelihood = {res: -839.0898}  
Iteration 3:{space 3}log pseudolikelihood = {res:-839.05484}  
Iteration 4:{space 3}log pseudolikelihood = {res:-839.05484}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-486.60088}  
Iteration 1:{space 3}log pseudolikelihood = {res:-430.34183}  
Iteration 2:{space 3}log pseudolikelihood = {res:-429.96601}  
Iteration 3:{space 3}log pseudolikelihood = {res:-429.96574}  
Iteration 4:{space 3}log pseudolikelihood = {res:-429.96574}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-413.67683}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-396.04481}  
Iteration 2:{space 3}log pseudolikelihood = {res:-384.49459}  
Iteration 3:{space 3}log pseudolikelihood = {res:-382.10449}  
Iteration 4:{space 3}log pseudolikelihood = {res:-382.05523}  
Iteration 5:{space 3}log pseudolikelihood = {res:-382.05511}  
Iteration 6:{space 3}log pseudolikelihood = {res:-382.05511}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     72.47
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-382.05511{txt}{col 49}Pseudo R2{col 67}= {res}    0.1114

{txt}{ralign 86:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}            CAT2_MIL{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}PTS_MISSION_MIL {c |}{col 22}{res}{space 2} .7291047{col 34}{space 2} .3181389{col 45}{space 1}    2.29{col 54}{space 3}0.022{col 62}{space 4} .1055638{col 75}{space 3} 1.352646
{txt}{space 5}GDP_MISSION_MIL {c |}{col 22}{res}{space 2}-.0000629{col 34}{space 2} .0000218{col 45}{space 1}   -2.89{col 54}{space 3}0.004{col 62}{space 4}-.0001055{col 75}{space 3}-.0000202
{txt}{space 10}GENDER_MIL {c |}{col 22}{res}{space 2}-17.08946{col 34}{space 2} 6.663389{col 45}{space 1}   -2.56{col 54}{space 3}0.010{col 62}{space 4}-30.14946{col 75}{space 3} -4.02946
{txt}{space 7}FORCE_DENSITY {c |}{col 22}{res}{space 2} .9292134{col 34}{space 2} .5812886{col 45}{space 1}    1.60{col 54}{space 3}0.110{col 62}{space 4}-.2100913{col 75}{space 3} 2.068518
{txt}PKO_FATALITIES_TOTAL {c |}{col 22}{res}{space 2}  .072436{col 34}{space 2} .0439905{col 45}{space 1}    1.65{col 54}{space 3}0.100{col 62}{space 4}-.0137839{col 75}{space 3} .1586559
{txt}{space 12}MANDATE3 {c |}{col 22}{res}{space 2} -.999183{col 34}{space 2} .5308021{col 45}{space 1}   -1.88{col 54}{space 3}0.060{col 62}{space 4}-2.039536{col 75}{space 3} .0411699
{txt}{space 6}ln_KM2_country {c |}{col 22}{res}{space 2}-.1345565{col 34}{space 2} .1788575{col 45}{space 1}   -0.75{col 54}{space 3}0.452{col 62}{space 4}-.4851108{col 75}{space 3} .2159978
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} 2.960657{col 34}{space 2} 2.547123{col 45}{space 1}    1.16{col 54}{space 3}0.245{col 62}{space 4}-2.031612{col 75}{space 3} 7.952926
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/lnalpha {c |}{col 22}{res}{space 2} .4542434{col 34}{space 2} .2375859{col 62}{space 4}-.0114163{col 75}{space 3} .9199031
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} 1.574981{col 34}{space 2} .3741933{col 62}{space 4} .9886486{col 75}{space 3} 2.509047
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 24

. nbreg CAT2_MIL CORRUPTION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1107.1862}  
Iteration 1:{space 3}log pseudolikelihood = {res:-900.64656}  
Iteration 2:{space 3}log pseudolikelihood = {res: -895.9236}  
Iteration 3:{space 3}log pseudolikelihood = {res:-895.89124}  
Iteration 4:{space 3}log pseudolikelihood = {res:-895.89124}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-486.60088}  
Iteration 1:{space 3}log pseudolikelihood = {res:-430.34183}  
Iteration 2:{space 3}log pseudolikelihood = {res:-429.96601}  
Iteration 3:{space 3}log pseudolikelihood = {res:-429.96574}  
Iteration 4:{space 3}log pseudolikelihood = {res:-429.96574}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -410.8928}  
Iteration 1:{space 3}log pseudolikelihood = {res: -387.6973}  
Iteration 2:{space 3}log pseudolikelihood = {res:-385.68833}  
Iteration 3:{space 3}log pseudolikelihood = {res:-385.64581}  
Iteration 4:{space 3}log pseudolikelihood = {res:-385.64579}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     96.49
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-385.64579{txt}{col 49}Pseudo R2{col 67}= {res}    0.1031

{txt}{ralign 86:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}            CAT2_MIL{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}CORRUPTION_MIL {c |}{col 22}{res}{space 2} -1.73395{col 34}{space 2} .3580483{col 45}{space 1}   -4.84{col 54}{space 3}0.000{col 62}{space 4}-2.435711{col 75}{space 3}-1.032188
{txt}{space 6}DEMOCRATIC_MIL {c |}{col 22}{res}{space 2}-.4913101{col 34}{space 2} .3838342{col 45}{space 1}   -1.28{col 54}{space 3}0.201{col 62}{space 4}-1.243611{col 75}{space 3} .2609911
{txt}{space 10}GENDER_MIL {c |}{col 22}{res}{space 2}-16.82236{col 34}{space 2} 6.146727{col 45}{space 1}   -2.74{col 54}{space 3}0.006{col 62}{space 4}-28.86972{col 75}{space 3}-4.774992
{txt}{space 7}FORCE_DENSITY {c |}{col 22}{res}{space 2}  1.70379{col 34}{space 2}  .593419{col 45}{space 1}    2.87{col 54}{space 3}0.004{col 62}{space 4} .5407105{col 75}{space 3}  2.86687
{txt}PKO_FATALITIES_TOTAL {c |}{col 22}{res}{space 2}   .09624{col 34}{space 2} .0483441{col 45}{space 1}    1.99{col 54}{space 3}0.047{col 62}{space 4} .0014873{col 75}{space 3} .1909928
{txt}{space 12}MANDATE3 {c |}{col 22}{res}{space 2}-.4736324{col 34}{space 2} .5872188{col 45}{space 1}   -0.81{col 54}{space 3}0.420{col 62}{space 4} -1.62456{col 75}{space 3} .6772953
{txt}{space 6}ln_KM2_country {c |}{col 22}{res}{space 2} .0783161{col 34}{space 2}   .15576{col 45}{space 1}    0.50{col 54}{space 3}0.615{col 62}{space 4}-.2269678{col 75}{space 3}    .3836
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} .6163045{col 34}{space 2} 2.004067{col 45}{space 1}    0.31{col 54}{space 3}0.758{col 62}{space 4}-3.311594{col 75}{space 3} 4.544203
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/lnalpha {c |}{col 22}{res}{space 2} .5017154{col 34}{space 2} .2206496{col 62}{space 4} .0692502{col 75}{space 3} .9341807
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} 1.651552{col 34}{space 2} .3644143{col 62}{space 4} 1.071704{col 75}{space 3} 2.545127
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 25

. nbreg CAT2_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_SIZE MANDATE3 GDP_HOST, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-636.40754}  
Iteration 1:{space 3}log pseudolikelihood = {res:-629.99389}  
Iteration 2:{space 3}log pseudolikelihood = {res:-629.98367}  
Iteration 3:{space 3}log pseudolikelihood = {res:-629.98367}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-432.53397}  
Iteration 1:{space 3}log pseudolikelihood = {res:-383.73854}  
Iteration 2:{space 3}log pseudolikelihood = {res:-383.34469}  
Iteration 3:{space 3}log pseudolikelihood = {res:-383.34436}  
Iteration 4:{space 3}log pseudolikelihood = {res:-383.34436}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-362.95465}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-343.30152}  
Iteration 2:{space 3}log pseudolikelihood = {res:-327.70005}  
Iteration 3:{space 3}log pseudolikelihood = {res:-325.52353}  
Iteration 4:{space 3}log pseudolikelihood = {res:-325.49419}  
Iteration 5:{space 3}log pseudolikelihood = {res:-325.49417}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       131
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     95.51
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-325.49417{txt}{col 49}Pseudo R2{col 67}= {res}    0.1509

{txt}{ralign 81:(Std. Err. adjusted for {res:24} clusters in ID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}       CAT2_MIL{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
PTS_MISSION_MIL {c |}{col 17}{res}{space 2} .8280628{col 29}{space 2} .3109748{col 40}{space 1}    2.66{col 49}{space 3}0.008{col 57}{space 4} .2185635{col 70}{space 3} 1.437562
{txt}{space 1}DEMOCRATIC_MIL {c |}{col 17}{res}{space 2} -.166154{col 29}{space 2} .3105055{col 40}{space 1}   -0.54{col 49}{space 3}0.593{col 57}{space 4}-.7747336{col 70}{space 3} .4424255
{txt}{space 5}GENDER_MIL {c |}{col 17}{res}{space 2}-12.05093{col 29}{space 2} 6.772812{col 40}{space 1}   -1.78{col 49}{space 3}0.075{col 57}{space 4} -25.3254{col 70}{space 3} 1.223536
{txt}{space 5}FORCE_SIZE {c |}{col 17}{res}{space 2} .0001955{col 29}{space 2} .0000486{col 40}{space 1}    4.02{col 49}{space 3}0.000{col 57}{space 4} .0001002{col 70}{space 3} .0002909
{txt}{space 7}MANDATE3 {c |}{col 17}{res}{space 2}-.3335356{col 29}{space 2} .5027922{col 40}{space 1}   -0.66{col 49}{space 3}0.507{col 57}{space 4} -1.31899{col 70}{space 3}  .651919
{txt}{space 7}GDP_HOST {c |}{col 17}{res}{space 2} .0000346{col 29}{space 2} .0000317{col 40}{space 1}    1.09{col 49}{space 3}0.276{col 57}{space 4}-.0000276{col 70}{space 3} .0000967
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-1.280913{col 29}{space 2} .7445474{col 40}{space 1}   -1.72{col 49}{space 3}0.085{col 57}{space 4}-2.740199{col 70}{space 3} .1783732
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/lnalpha {c |}{col 17}{res}{space 2}   .04341{col 29}{space 2} .2261603{col 57}{space 4} -.399856{col 70}{space 3} .4866759
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          alpha {c |}{col 17}{res}{space 2} 1.044366{col 29}{space 2} .2361941{col 57}{space 4} .6704166{col 70}{space 3} 1.626899
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 26

. nbreg CAT2_MIL PTS_MISSION_MIL GDP_MISSION_MIL GENDER_MIL FORCE_SIZE MANDATE3 GDP_HOST, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-632.02383}  
Iteration 1:{space 3}log pseudolikelihood = {res:-623.31969}  
Iteration 2:{space 3}log pseudolikelihood = {res:-623.29531}  
Iteration 3:{space 3}log pseudolikelihood = {res:-623.29531}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-432.53397}  
Iteration 1:{space 3}log pseudolikelihood = {res:-383.73854}  
Iteration 2:{space 3}log pseudolikelihood = {res:-383.34469}  
Iteration 3:{space 3}log pseudolikelihood = {res:-383.34436}  
Iteration 4:{space 3}log pseudolikelihood = {res:-383.34436}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-361.43647}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-337.31501}  
Iteration 2:{space 3}log pseudolikelihood = {res: -322.4838}  
Iteration 3:{space 3}log pseudolikelihood = {res: -321.1304}  
Iteration 4:{space 3}log pseudolikelihood = {res:-321.10702}  
Iteration 5:{space 3}log pseudolikelihood = {res:-321.10702}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       131
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}    125.42
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-321.10702{txt}{col 49}Pseudo R2{col 67}= {res}    0.1624

{txt}{ralign 81:(Std. Err. adjusted for {res:24} clusters in ID)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}       CAT2_MIL{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
PTS_MISSION_MIL {c |}{col 17}{res}{space 2} .4272522{col 29}{space 2} .3288336{col 40}{space 1}    1.30{col 49}{space 3}0.194{col 57}{space 4}-.2172498{col 70}{space 3} 1.071754
{txt}GDP_MISSION_MIL {c |}{col 17}{res}{space 2}-.0000489{col 29}{space 2} .0000211{col 40}{space 1}   -2.32{col 49}{space 3}0.020{col 57}{space 4}-.0000903{col 70}{space 3}-7.58e-06
{txt}{space 5}GENDER_MIL {c |}{col 17}{res}{space 2}-13.86722{col 29}{space 2} 5.510291{col 40}{space 1}   -2.52{col 49}{space 3}0.012{col 57}{space 4}-24.66719{col 70}{space 3} -3.06725
{txt}{space 5}FORCE_SIZE {c |}{col 17}{res}{space 2} .0001725{col 29}{space 2} .0000499{col 40}{space 1}    3.46{col 49}{space 3}0.001{col 57}{space 4} .0000748{col 70}{space 3} .0002703
{txt}{space 7}MANDATE3 {c |}{col 17}{res}{space 2}-.4609907{col 29}{space 2} .5476725{col 40}{space 1}   -0.84{col 49}{space 3}0.400{col 57}{space 4}-1.534409{col 70}{space 3} .6124277
{txt}{space 7}GDP_HOST {c |}{col 17}{res}{space 2} .0000588{col 29}{space 2} .0000282{col 40}{space 1}    2.09{col 49}{space 3}0.037{col 57}{space 4} 3.57e-06{col 70}{space 3} .0001141
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} .3179925{col 29}{space 2} .6992134{col 40}{space 1}    0.45{col 49}{space 3}0.649{col 57}{space 4}-1.052441{col 70}{space 3} 1.688426
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 7}/lnalpha {c |}{col 17}{res}{space 2}-.0110672{col 29}{space 2} .2050415{col 57}{space 4}-.4129413{col 70}{space 3} .3908068
{txt}{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
          alpha {c |}{col 17}{res}{space 2} .9889938{col 29}{space 2} .2027848{col 57}{space 4} .6617011{col 70}{space 3} 1.478173
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 27

. nbreg CAT2_MIL CORRUPTION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_SIZE MANDATE3 GDP_HOST, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-606.71134}  
Iteration 1:{space 3}log pseudolikelihood = {res: -603.1017}  
Iteration 2:{space 3}log pseudolikelihood = {res:-603.09036}  
Iteration 3:{space 3}log pseudolikelihood = {res:-603.09036}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-432.53397}  
Iteration 1:{space 3}log pseudolikelihood = {res:-383.73854}  
Iteration 2:{space 3}log pseudolikelihood = {res:-383.34469}  
Iteration 3:{space 3}log pseudolikelihood = {res:-383.34436}  
Iteration 4:{space 3}log pseudolikelihood = {res:-383.34436}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-360.59008}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res: -336.2075}  
Iteration 2:{space 3}log pseudolikelihood = {res:-324.00842}  
Iteration 3:{space 3}log pseudolikelihood = {res:-315.95443}  
Iteration 4:{space 3}log pseudolikelihood = {res:-315.22821}  
Iteration 5:{space 3}log pseudolikelihood = {res:-315.22439}  
Iteration 6:{space 3}log pseudolikelihood = {res:-315.22439}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       131
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}    200.78
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-315.22439{txt}{col 49}Pseudo R2{col 67}= {res}    0.1777

{txt}{ralign 80:(Std. Err. adjusted for {res:24} clusters in ID)}
{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}      CAT2_MIL{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
CORRUPTION_MIL {c |}{col 16}{res}{space 2}-1.518935{col 28}{space 2} .2624626{col 39}{space 1}   -5.79{col 48}{space 3}0.000{col 56}{space 4}-2.033352{col 69}{space 3}-1.004518
{txt}DEMOCRATIC_MIL {c |}{col 16}{res}{space 2}-.6105596{col 28}{space 2} .2192004{col 39}{space 1}   -2.79{col 48}{space 3}0.005{col 56}{space 4}-1.040184{col 69}{space 3}-.1809347
{txt}{space 4}GENDER_MIL {c |}{col 16}{res}{space 2}-10.77464{col 28}{space 2}  4.18241{col 39}{space 1}   -2.58{col 48}{space 3}0.010{col 56}{space 4}-18.97201{col 69}{space 3}-2.577266
{txt}{space 4}FORCE_SIZE {c |}{col 16}{res}{space 2} .0002112{col 28}{space 2} .0000313{col 39}{space 1}    6.74{col 48}{space 3}0.000{col 56}{space 4} .0001498{col 69}{space 3} .0002726
{txt}{space 6}MANDATE3 {c |}{col 16}{res}{space 2} -.200909{col 28}{space 2} .3156173{col 39}{space 1}   -0.64{col 48}{space 3}0.524{col 56}{space 4}-.8195075{col 69}{space 3} .4176895
{txt}{space 6}GDP_HOST {c |}{col 16}{res}{space 2}  .000026{col 28}{space 2} .0000189{col 39}{space 1}    1.37{col 48}{space 3}0.171{col 56}{space 4}-.0000112{col 69}{space 3} .0000631
{txt}{space 9}_cons {c |}{col 16}{res}{space 2} .2135073{col 28}{space 2} .4758024{col 39}{space 1}    0.45{col 48}{space 3}0.654{col 56}{space 4}-.7190483{col 69}{space 3} 1.146063
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}/lnalpha {c |}{col 16}{res}{space 2}-.1517674{col 28}{space 2} .1721883{col 56}{space 4}-.4892503{col 69}{space 3} .1857154
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
         alpha {c |}{col 16}{res}{space 2} .8591881{col 28}{space 2} .1479421{col 56}{space 4} .6130859{col 69}{space 3}  1.20408
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. **Note: Reproduction of these figures requires installation of the Travis Braidwood CLEAR-PLOT package available at http://travisbraidwood.altervista.org/dataverse.html.

. 
. *Figure 1. Expected Category 1 allegations among PKO military forces including SEAs, by the weighted Political Terror Scores of the PKO military force. 

. estsimp nbreg CAT1_SEA_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

{txt}Fitting Poisson model:

Iteration 0:{col 16}log pseudolikelihood = {res}-941.83988{txt}  
Iteration 1:{col 16}log pseudolikelihood = {res}-575.53962{txt}  
Iteration 2:{col 16}log pseudolikelihood = {res}-567.09605{txt}  
Iteration 3:{col 16}log pseudolikelihood = {res}-567.05054{txt}  
Iteration 4:{col 16}log pseudolikelihood = {res}-567.05054{txt}  
{res}
{txt}Fitting constant-only model:

Iteration 0:{col 16}log pseudolikelihood = {res}-404.89038{txt}  
Iteration 1:{col 16}log pseudolikelihood = {res}-356.12068{txt}  
Iteration 2:{col 16}log pseudolikelihood = {res}-356.11763{txt}  
Iteration 3:{col 16}log pseudolikelihood = {res}-356.11763{txt}  
{res}
{txt}Fitting full model:

Iteration 0:{col 16}log pseudolikelihood = {res}-346.07002{txt}  (not concave)
Iteration 1:{col 16}log pseudolikelihood = {res}-328.21241{txt}  
Iteration 2:{col 16}log pseudolikelihood = {res}  -319.179{txt}  
Iteration 3:{col 16}log pseudolikelihood = {res}-312.32864{txt}  
Iteration 4:{col 16}log pseudolikelihood = {res} -312.2561{txt}  
Iteration 5:{col 16}log pseudolikelihood = {res}-312.25599{txt}  
Iteration 6:{col 16}log pseudolikelihood = {res}-312.25599{txt}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     57.76
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-312.25599{txt}{col 49}Pseudo R2{col 67}= {res}    0.1232

{txt}{ralign 86:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}        CAT1_SEA_MIL{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}PTS_MISSION_MIL {c |}{col 22}{res}{space 2} 1.151343{col 34}{space 2} .3497643{col 45}{space 1}    3.29{col 54}{space 3}0.001{col 62}{space 4} .4658176{col 75}{space 3} 1.836868
{txt}{space 6}DEMOCRATIC_MIL {c |}{col 22}{res}{space 2}-.0379812{col 34}{space 2} .2988585{col 45}{space 1}   -0.13{col 54}{space 3}0.899{col 62}{space 4} -.623733{col 75}{space 3} .5477706
{txt}{space 10}GENDER_MIL {c |}{col 22}{res}{space 2}-16.15391{col 34}{space 2} 8.856818{col 45}{space 1}   -1.82{col 54}{space 3}0.068{col 62}{space 4}-33.51295{col 75}{space 3} 1.205135
{txt}{space 7}FORCE_DENSITY {c |}{col 22}{res}{space 2} 1.473337{col 34}{space 2} .8511924{col 45}{space 1}    1.73{col 54}{space 3}0.083{col 62}{space 4}-.1949696{col 75}{space 3} 3.141643
{txt}PKO_FATALITIES_TOTAL {c |}{col 22}{res}{space 2} .0612265{col 34}{space 2} .0461934{col 45}{space 1}    1.33{col 54}{space 3}0.185{col 62}{space 4} -.029311{col 75}{space 3} .1517639
{txt}{space 12}MANDATE3 {c |}{col 22}{res}{space 2} .1528753{col 34}{space 2} .5410232{col 45}{space 1}    0.28{col 54}{space 3}0.778{col 62}{space 4}-.9075108{col 75}{space 3} 1.213261
{txt}{space 6}ln_KM2_country {c |}{col 22}{res}{space 2} .1153239{col 34}{space 2} .1617285{col 45}{space 1}    0.71{col 54}{space 3}0.476{col 62}{space 4}-.2016582{col 75}{space 3}  .432306
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-3.093769{col 34}{space 2} 1.961723{col 45}{space 1}   -1.58{col 54}{space 3}0.115{col 62}{space 4}-6.938675{col 75}{space 3} .7511381
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /lnalpha {c |}{col 22}{res}{space 2} .4078693{col 34}{space 2} .1947884{col 62}{space 4} .0260909{col 75}{space 3} .7896476
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} 1.503611{col 34}{space 2}  .292886{col 62}{space 4} 1.026434{col 75}{space 3}  2.20262
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{res}Simulating main parameters.  Please wait....

Note: Clarify is expanding your dataset from 218 observations to 1000
observations in order to accommodate the simulations.  This will append
missing values to the bottom of your original dataset.

% of simulations completed: 11% 22% 33% 44% 55% 66% 77% 88% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9

{com}. preserve

. local a =0 

. setx mean 

. setx PTS_MISSION_MIL (`a') 

. simqi, level(95)

{txt}      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
               E(CAT1_S~L) |  {res} .2663657     .2529514     .0408779    .9758351

{com}. macro list
{txt}{p 0 16}
mrt_seto:{space 7}{res}{res},
{p_end}
{txt}{p 0 16}
mrt_vt:{space 9}{res}{res}(0) mean mean mean mean mean mean
{p_end}
{txt}{p 0 16}
S_FNDATE:{space 7}{res}{res} 6 Jun 2019 12:38
{p_end}
{txt}{p 0 16}
S_FN:{space 11}{res}{res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/PKAT_ISQ_rep_data.dta
{p_end}
{txt}{p 0 16}
ML_setes:{space 7}{res}{res}yes
{p_end}
{txt}{p 0 16}
S_2:{space 12}{res}{res}1
{p_end}
{txt}{p 0 16}
S_level:{space 8}{res}{res}95
{p_end}
{txt}{p 0 16}
F1:{space 13}{res}{res}help advice;
{p_end}
{txt}{p 0 16}
F2:{space 13}{res}{res}describe;
{p_end}
{txt}{p 0 16}
F7:{space 13}{res}{res}save 
{p_end}
{txt}{p 0 16}
F8:{space 13}{res}{res}use 
{p_end}
{txt}{p 0 16}
S_ADO:{space 10}{res}{res}BASE;SITE;.;PERSONAL;PLUS;OLDPLACE
{p_end}
{txt}{p 0 16}
S_FLAVOR:{space 7}{res}{res}Intercooled
{p_end}
{txt}{p 0 16}
S_OS:{space 11}{res}{res}MacOSX
{p_end}
{txt}{p 0 16}
S_OSDTL:{space 8}{res}{res}10.13.6
{p_end}
{txt}{p 0 16}
S_MACH:{space 9}{res}{res}Macintosh (Intel 64-bit)
{p_end}
{txt}{p 0 16}
_a:{space 13}{res}{res}0
{p_end}

{com}. scalar list 
{txt}     sderr = {res} .25295135
{txt}      Pr0U = {res} .02416492
{txt}      Pr0L = {res} .95912213
{txt}       Pr0 = {res} .73363428
{txt}        Pr = {res} .26636572
{txt}       PrU = {res} .97583508
{txt}       PrL = {res} .04087787

{com}. postutil clear

. postfile mypost prediction upper lower using simresults, replace 
{txt}(note: file simresults.dta not found)

{com}. noisily display "start"
{res}start

{com}. set obs 10000 
{txt}{p}
number of observations (_N)  was 1,000,
now 10,000
{p_end}

{com}. while `a' <= 3  {c -(}
{txt}  2{com}. qui simqi , level(95)
{txt}  3{com}. scalar prediction= Pr
{txt}  4{com}. scalar upper = PrU
{txt}  5{com}. scalar lower = PrL
{txt}  6{com}. post mypost (prediction) (upper) (lower)
{txt}  7{com}. scalar drop prediction upper lower
{txt}  8{com}. local a = `a'+.005
{txt}  9{com}. setx PTS_MISSION_MIL (`a') 
{txt} 10{com}. display "." _c 
{txt} 11{com}. {c )-}
.........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
. display ""
{res}

{com}. postclose mypost 

. use simresults, clear 

. sum

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}prediction {c |}{res}        601    1.755034    1.567812   .2663657   6.273233
{txt}{space 7}upper {c |}{res}        601    3.139313    2.615741   .9758351   11.97075
{txt}{space 7}lower {c |}{res}        601    .9459835    .9021234   .0408779   2.994188

{com}. gen MV = 0+.005*(_n-1) 

. gsort prediction upper lower -MV 

. graph twoway  line prediction MV, clwidth(medium) clcolor(black) clpattern(solid) sort || line lower MV, clpattern(dash) clwidth(thin) clcolor(black) sort || line upper  MV, clpattern(dash) clwidth(thin) clcolor(black) sort 
{res}
{com}. 
. 
. log off
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/Misconduct_ISQ_replication_log.smcl
  {txt}log type:  {res}smcl
 {txt}paused on:  {res} 7 Jun 2019, 19:50:45
{txt}{.-}
{smcl}
{txt}{sf}{ul off}{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/Misconduct_ISQ_replication_log.smcl
  {txt}log type:  {res}smcl
{txt}resumed on:  {res} 7 Jun 2019, 19:51:34

{com}. *Figure 2. Expected Category 1 allegations among PKO military forces including SEAs, by the weighted Corruption Scores of the PKO military force.

. **Generate the graph using Model 6, above.

. estsimp nbreg CAT1_SEA_MIL CORRUPTION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

{txt}Fitting Poisson model:

Iteration 0:{col 16}log pseudolikelihood = {res}-1058.0108{txt}  
Iteration 1:{col 16}log pseudolikelihood = {res}-597.47131{txt}  
Iteration 2:{col 16}log pseudolikelihood = {res}-584.95035{txt}  
Iteration 3:{col 16}log pseudolikelihood = {res}-584.78955{txt}  
Iteration 4:{col 16}log pseudolikelihood = {res}-584.78953{txt}  
{res}
{txt}Fitting constant-only model:

Iteration 0:{col 16}log pseudolikelihood = {res}-404.89038{txt}  
Iteration 1:{col 16}log pseudolikelihood = {res}-356.12068{txt}  
Iteration 2:{col 16}log pseudolikelihood = {res}-356.11763{txt}  
Iteration 3:{col 16}log pseudolikelihood = {res}-356.11763{txt}  
{res}
{txt}Fitting full model:

Iteration 0:{col 16}log pseudolikelihood = {res}-338.74697{txt}  
Iteration 1:{col 16}log pseudolikelihood = {res}-336.75056{txt}  (backed up)
Iteration 2:{col 16}log pseudolikelihood = {res} -316.4345{txt}  
Iteration 3:{col 16}log pseudolikelihood = {res}-311.64964{txt}  
Iteration 4:{col 16}log pseudolikelihood = {res} -311.6206{txt}  
Iteration 5:{col 16}log pseudolikelihood = {res}-311.62059{txt}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     93.22
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-311.62059{txt}{col 49}Pseudo R2{col 67}= {res}    0.1250

{txt}{ralign 86:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}        CAT1_SEA_MIL{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}CORRUPTION_MIL {c |}{col 22}{res}{space 2} -1.51664{col 34}{space 2} .3713437{col 45}{space 1}   -4.08{col 54}{space 3}0.000{col 62}{space 4}-2.244461{col 75}{space 3}  -.78882
{txt}{space 6}DEMOCRATIC_MIL {c |}{col 22}{res}{space 2}-.3691407{col 34}{space 2} .3089807{col 45}{space 1}   -1.19{col 54}{space 3}0.232{col 62}{space 4}-.9747318{col 75}{space 3} .2364503
{txt}{space 10}GENDER_MIL {c |}{col 22}{res}{space 2}-16.21121{col 34}{space 2} 7.408066{col 45}{space 1}   -2.19{col 54}{space 3}0.029{col 62}{space 4}-30.73075{col 75}{space 3}-1.691667
{txt}{space 7}FORCE_DENSITY {c |}{col 22}{res}{space 2} 1.831051{col 34}{space 2} .9162489{col 45}{space 1}    2.00{col 54}{space 3}0.046{col 62}{space 4} .0352363{col 75}{space 3} 3.626866
{txt}PKO_FATALITIES_TOTAL {c |}{col 22}{res}{space 2}  .066655{col 34}{space 2} .0444348{col 45}{space 1}    1.50{col 54}{space 3}0.134{col 62}{space 4}-.0204356{col 75}{space 3} .1537456
{txt}{space 12}MANDATE3 {c |}{col 22}{res}{space 2} .5214393{col 34}{space 2} .5293178{col 45}{space 1}    0.99{col 54}{space 3}0.325{col 62}{space 4}-.5160045{col 75}{space 3} 1.558883
{txt}{space 6}ln_KM2_country {c |}{col 22}{res}{space 2} .1490971{col 34}{space 2} .1573156{col 45}{space 1}    0.95{col 54}{space 3}0.343{col 62}{space 4}-.1592358{col 75}{space 3}   .45743
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-1.351084{col 34}{space 2} 1.973875{col 45}{space 1}   -0.68{col 54}{space 3}0.494{col 62}{space 4}-5.219807{col 75}{space 3} 2.517639
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /lnalpha {c |}{col 22}{res}{space 2} .3647814{col 34}{space 2} .2683633{col 62}{space 4} -.161201{col 75}{space 3} .8907638
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} 1.440199{col 34}{space 2} .3864966{col 62}{space 4}  .851121{col 75}{space 3}  2.43699
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{res}Simulating main parameters.  Please wait....

Note: Clarify is expanding your dataset from 218 observations to 1000
observations in order to accommodate the simulations.  This will append
missing values to the bottom of your original dataset.

% of simulations completed: 11% 22% 33% 44% 55% 66% 77% 88% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9

{com}. preserve
{err}already preserved
{txt}{search r(621), local:r(621);}

{com}. local a =-1 

. setx mean 

. setx CORRUPTION_MIL (`a') 

. simqi, level(95)

{txt}      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
               E(CAT1_S~L) |  {res} 10.38583     4.177493     4.517179    19.85406

{com}. macro list
{txt}{p 0 16}
mrt_seto:{space 7}{res}{res},
{p_end}
{txt}{p 0 16}
mrt_vt:{space 9}{res}{res}(-1) mean mean mean mean mean mean
{p_end}
{txt}{p 0 16}
ML_setes:{space 7}{res}{res}yes
{p_end}
{txt}{p 0 16}
S_2:{space 12}{res}{res}1
{p_end}
{txt}{p 0 16}
S_FNDATE:{space 7}{res}{res} 6 Jun 2019 12:38
{p_end}
{txt}{p 0 16}
S_FN:{space 11}{res}{res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/PKAT_ISQ_rep_data.dta
{p_end}
{txt}{p 0 16}
T_gm_fix_span:{space 2}{res}{res}0
{p_end}
{txt}{p 0 16}
S_level:{space 8}{res}{res}95
{p_end}
{txt}{p 0 16}
F1:{space 13}{res}{res}help advice;
{p_end}
{txt}{p 0 16}
F2:{space 13}{res}{res}describe;
{p_end}
{txt}{p 0 16}
F7:{space 13}{res}{res}save 
{p_end}
{txt}{p 0 16}
F8:{space 13}{res}{res}use 
{p_end}
{txt}{p 0 16}
S_ADO:{space 10}{res}{res}BASE;SITE;.;PERSONAL;PLUS;OLDPLACE
{p_end}
{txt}{p 0 16}
S_FLAVOR:{space 7}{res}{res}Intercooled
{p_end}
{txt}{p 0 16}
S_OS:{space 11}{res}{res}MacOSX
{p_end}
{txt}{p 0 16}
S_OSDTL:{space 8}{res}{res}10.13.6
{p_end}
{txt}{p 0 16}
S_MACH:{space 9}{res}{res}Macintosh (Intel 64-bit)
{p_end}
{txt}{p 0 16}
_a:{space 13}{res}{res}-1
{p_end}

{com}. scalar list 
{txt}     sderr = {res} 4.1774927
{txt}      Pr0U = {res}-18.854064
{txt}      Pr0L = {res}-3.5171788
{txt}       Pr0 = {res}-9.3858335
{txt}        Pr = {res} 10.385834
{txt}       PrU = {res} 19.854064
{txt}       PrL = {res} 4.5171788

{com}. postutil clear

. postfile mypost prediction upper lower using simresults, replace 

. noisily display "start"
{res}start

{com}. set obs 10000 
{txt}{p}
number of observations (_N)  was 1,000,
now 10,000
{p_end}

{com}. while `a' <= 1  {c -(}
{txt}  2{com}. qui simqi , level(95)
{txt}  3{com}. scalar prediction= Pr
{txt}  4{com}. scalar upper = PrU
{txt}  5{com}. scalar lower = PrL
{txt}  6{com}. post mypost (prediction) (upper) (lower)
{txt}  7{com}. scalar drop prediction upper lower
{txt}  8{com}. local a = `a'+.005
{txt}  9{com}. setx CORRUPTION_MIL (`a') 
{txt} 10{com}. display "." _c 
{txt} 11{com}. {c )-}
................................................................................................................................................................................................................................................................................................................................................................................................................
. display ""
{res}

{com}. postclose mypost 

. use simresults, clear 

. sum

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}prediction {c |}{res}        400    3.158213    2.637937   .5140612   10.38583
{txt}{space 7}upper {c |}{res}        400    5.391701    4.825663   1.151905   19.85406
{txt}{space 7}lower {c |}{res}        400    1.770515    1.309007   .1946528   4.517179

{com}. gen MV = 0+.005*(_n-1) 

. gsort prediction upper lower -MV 

. graph twoway  line prediction MV, clwidth(medium) clcolor(black) clpattern(solid) sort || line lower MV, clpattern(dash) clwidth(thin) clcolor(black) sort || line upper  MV, clpattern(dash) clwidth(thin) clcolor(black) sort 
{res}
{com}. 
. log off
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/Misconduct_ISQ_replication_log.smcl
  {txt}log type:  {res}smcl
 {txt}paused on:  {res} 7 Jun 2019, 19:51:50
{txt}{.-}
{smcl}
{txt}{sf}{ul off}{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/Misconduct_ISQ_replication_log.smcl
  {txt}log type:  {res}smcl
{txt}resumed on:  {res} 7 Jun 2019, 19:52:04

{com}. *Figure 3. Expected Category 1 allegations among PKO military forces excluding SEAs, by the weighted Political Terror Scores of the PKO military force.

. estsimp nbreg CAT1_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

{txt}Fitting Poisson model:

Iteration 0:{col 16}log pseudolikelihood = {res}-1200.5037{txt}  
Iteration 1:{col 16}log pseudolikelihood = {res} -616.9813{txt}  
Iteration 2:{col 16}log pseudolikelihood = {res}-365.48047{txt}  
Iteration 3:{col 16}log pseudolikelihood = {res}-347.52114{txt}  
Iteration 4:{col 16}log pseudolikelihood = {res}-347.11443{txt}  
Iteration 5:{col 16}log pseudolikelihood = {res}-347.11417{txt}  
Iteration 6:{col 16}log pseudolikelihood = {res}-347.11417{txt}  
{res}
{txt}Fitting constant-only model:

Iteration 0:{col 16}log pseudolikelihood = {res}-327.36556{txt}  
Iteration 1:{col 16}log pseudolikelihood = {res}-297.94672{txt}  
Iteration 2:{col 16}log pseudolikelihood = {res}-297.83249{txt}  
Iteration 3:{col 16}log pseudolikelihood = {res}-297.83248{txt}  
{res}
{txt}Fitting full model:

Iteration 0:{col 16}log pseudolikelihood = {res}-283.05687{txt}  (not concave)
Iteration 1:{col 16}log pseudolikelihood = {res} -265.0878{txt}  
Iteration 2:{col 16}log pseudolikelihood = {res} -260.9361{txt}  
Iteration 3:{col 16}log pseudolikelihood = {res}-254.66588{txt}  
Iteration 4:{col 16}log pseudolikelihood = {res}-254.45491{txt}  
Iteration 5:{col 16}log pseudolikelihood = {res}-254.45448{txt}  
Iteration 6:{col 16}log pseudolikelihood = {res}-254.45448{txt}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}    108.24
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-254.45448{txt}{col 49}Pseudo R2{col 67}= {res}    0.1456

{txt}{ralign 86:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}            CAT1_MIL{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}PTS_MISSION_MIL {c |}{col 22}{res}{space 2} .9308142{col 34}{space 2} .2664565{col 45}{space 1}    3.49{col 54}{space 3}0.000{col 62}{space 4} .4085689{col 75}{space 3} 1.453059
{txt}{space 6}DEMOCRATIC_MIL {c |}{col 22}{res}{space 2} .1330209{col 34}{space 2} .2156443{col 45}{space 1}    0.62{col 54}{space 3}0.537{col 62}{space 4}-.2896341{col 75}{space 3} .5556759
{txt}{space 10}GENDER_MIL {c |}{col 22}{res}{space 2}-20.97347{col 34}{space 2} 8.029033{col 45}{space 1}   -2.61{col 54}{space 3}0.009{col 62}{space 4}-36.71009{col 75}{space 3}-5.236857
{txt}{space 7}FORCE_DENSITY {c |}{col 22}{res}{space 2} 1.789702{col 34}{space 2} .5630486{col 45}{space 1}    3.18{col 54}{space 3}0.001{col 62}{space 4} .6861467{col 75}{space 3} 2.893257
{txt}PKO_FATALITIES_TOTAL {c |}{col 22}{res}{space 2} .0430086{col 34}{space 2} .0250464{col 45}{space 1}    1.72{col 54}{space 3}0.086{col 62}{space 4}-.0060814{col 75}{space 3} .0920986
{txt}{space 12}MANDATE3 {c |}{col 22}{res}{space 2} .3068129{col 34}{space 2} .3860842{col 45}{space 1}    0.79{col 54}{space 3}0.427{col 62}{space 4}-.4498981{col 75}{space 3} 1.063524
{txt}{space 6}ln_KM2_country {c |}{col 22}{res}{space 2} .0775027{col 34}{space 2} .1110071{col 45}{space 1}    0.70{col 54}{space 3}0.485{col 62}{space 4}-.1400672{col 75}{space 3} .2950725
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-2.547963{col 34}{space 2} 1.408236{col 45}{space 1}   -1.81{col 54}{space 3}0.070{col 62}{space 4}-5.308054{col 75}{space 3} .2121283
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /lnalpha {c |}{col 22}{res}{space 2} .1066773{col 34}{space 2} .1982254{col 62}{space 4}-.2818373{col 75}{space 3} .4951919
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} 1.112575{col 34}{space 2} .2205406{col 62}{space 4} .7543964{col 75}{space 3} 1.640813
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{res}Simulating main parameters.  Please wait....

Note: Clarify is expanding your dataset from 218 observations to 1000
observations in order to accommodate the simulations.  This will append
missing values to the bottom of your original dataset.

% of simulations completed: 11% 22% 33% 44% 55% 66% 77% 88% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9

{com}. preserve
{err}already preserved
{txt}{search r(621), local:r(621);}

{com}. local a =0 

. setx mean 

. setx PTS_MISSION_MIL (`a') 

. simqi, level(95)

{txt}      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
               E(CAT1_MIL) |  {res} .1972585     .1280795     .0464266    .5221027

{com}. macro list
{txt}{p 0 16}
mrt_seto:{space 7}{res}{res},
{p_end}
{txt}{p 0 16}
mrt_vt:{space 9}{res}{res}(0) mean mean mean mean mean mean
{p_end}
{txt}{p 0 16}
ML_setes:{space 7}{res}{res}yes
{p_end}
{txt}{p 0 16}
S_2:{space 12}{res}{res}1
{p_end}
{txt}{p 0 16}
S_FNDATE:{space 7}{res}{res} 6 Jun 2019 12:38
{p_end}
{txt}{p 0 16}
S_FN:{space 11}{res}{res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/PKAT_ISQ_rep_data.dta
{p_end}
{txt}{p 0 16}
T_gm_fix_span:{space 2}{res}{res}0
{p_end}
{txt}{p 0 16}
S_level:{space 8}{res}{res}95
{p_end}
{txt}{p 0 16}
F1:{space 13}{res}{res}help advice;
{p_end}
{txt}{p 0 16}
F2:{space 13}{res}{res}describe;
{p_end}
{txt}{p 0 16}
F7:{space 13}{res}{res}save 
{p_end}
{txt}{p 0 16}
F8:{space 13}{res}{res}use 
{p_end}
{txt}{p 0 16}
S_ADO:{space 10}{res}{res}BASE;SITE;.;PERSONAL;PLUS;OLDPLACE
{p_end}
{txt}{p 0 16}
S_FLAVOR:{space 7}{res}{res}Intercooled
{p_end}
{txt}{p 0 16}
S_OS:{space 11}{res}{res}MacOSX
{p_end}
{txt}{p 0 16}
S_OSDTL:{space 8}{res}{res}10.13.6
{p_end}
{txt}{p 0 16}
S_MACH:{space 9}{res}{res}Macintosh (Intel 64-bit)
{p_end}
{txt}{p 0 16}
_a:{space 13}{res}{res}0
{p_end}

{com}. scalar list 
{txt}     sderr = {res} .12807955
{txt}      Pr0U = {res} .47789729
{txt}      Pr0L = {res}  .9535734
{txt}       Pr0 = {res} .80274151
{txt}        Pr = {res} .19725849
{txt}       PrU = {res} .52210271
{txt}       PrL = {res}  .0464266

{com}. postutil clear

. postfile mypost prediction upper lower using simresults, replace 

. noisily display "start"
{res}start

{com}. set obs 10000 
{txt}{p}
number of observations (_N)  was 1,000,
now 10,000
{p_end}

{com}. while `a' <= 4  {c -(}
{txt}  2{com}. qui simqi , level(95)
{txt}  3{com}. scalar prediction= Pr
{txt}  4{com}. scalar upper = PrU
{txt}  5{com}. scalar lower = PrL
{txt}  6{com}. post mypost (prediction) (upper) (lower)
{txt}  7{com}. scalar drop prediction upper lower
{txt}  8{com}. local a = `a'+.005
{txt}  9{com}. setx PTS_MISSION_MIL (`a') 
{txt} 10{com}. display "." _c 
{txt} 11{com}. {c )-}
.................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
. display ""
{res}

{com}. postclose mypost 

. use simresults, clear 

. sum

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}prediction {c |}{res}        801    2.004556    2.061037   .1972585   8.292565
{txt}{space 7}upper {c |}{res}        801     3.75193    4.550756   .5221027   19.86001
{txt}{space 7}lower {c |}{res}        801    .9907649    .8313543   .0464266   2.758874

{com}. gen MV = 0+.005*(_n-1) 

. gsort prediction upper lower -MV 

. graph twoway  line prediction MV, clwidth(medium) clcolor(black) clpattern(solid) sort || line lower MV, clpattern(dash) clwidth(thin) clcolor(black) sort || line upper  MV, clpattern(dash) clwidth(thin) clcolor(black) sort 
{res}
{com}. 
. log off
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/Misconduct_ISQ_replication_log.smcl
  {txt}log type:  {res}smcl
 {txt}paused on:  {res} 7 Jun 2019, 19:52:34
{txt}{.-}
{smcl}
{txt}{sf}{ul off}{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/Misconduct_ISQ_replication_log.smcl
  {txt}log type:  {res}smcl
{txt}resumed on:  {res} 7 Jun 2019, 19:53:02

{com}. *Figure 4. Expected Category 1 allegations among PKO military forces excluding SEAs, by the weighted Corruption Scores of the PKO military force.

. **Generate the graph using Model 15, above.

. estsimp nbreg CAT1_MIL CORRUPTION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

{txt}Fitting Poisson model:

Iteration 0:{col 16}log pseudolikelihood = {res}-1233.7137{txt}  
Iteration 1:{col 16}log pseudolikelihood = {res}-713.82163{txt}  
Iteration 2:{col 16}log pseudolikelihood = {res}-387.70061{txt}  
Iteration 3:{col 16}log pseudolikelihood = {res}-363.65952{txt}  
Iteration 4:{col 16}log pseudolikelihood = {res}-363.10127{txt}  
Iteration 5:{col 16}log pseudolikelihood = {res}-363.10029{txt}  
Iteration 6:{col 16}log pseudolikelihood = {res}-363.10029{txt}  
{res}
{txt}Fitting constant-only model:

Iteration 0:{col 16}log pseudolikelihood = {res}-327.36556{txt}  
Iteration 1:{col 16}log pseudolikelihood = {res}-297.94672{txt}  
Iteration 2:{col 16}log pseudolikelihood = {res}-297.83249{txt}  
Iteration 3:{col 16}log pseudolikelihood = {res}-297.83248{txt}  
{res}
{txt}Fitting full model:

Iteration 0:{col 16}log pseudolikelihood = {res}-281.71143{txt}  
Iteration 1:{col 16}log pseudolikelihood = {res}-267.40363{txt}  
Iteration 2:{col 16}log pseudolikelihood = {res} -259.1022{txt}  
Iteration 3:{col 16}log pseudolikelihood = {res}  -258.023{txt}  
Iteration 4:{col 16}log pseudolikelihood = {res}-258.01839{txt}  
Iteration 5:{col 16}log pseudolikelihood = {res}-258.01839{txt}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}    115.67
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-258.01839{txt}{col 49}Pseudo R2{col 67}= {res}    0.1337

{txt}{ralign 86:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}            CAT1_MIL{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}CORRUPTION_MIL {c |}{col 22}{res}{space 2}-.8718004{col 34}{space 2}  .331849{col 45}{space 1}   -2.63{col 54}{space 3}0.009{col 62}{space 4}-1.522212{col 75}{space 3}-.2213884
{txt}{space 6}DEMOCRATIC_MIL {c |}{col 22}{res}{space 2} .0897158{col 34}{space 2} .2661629{col 45}{space 1}    0.34{col 54}{space 3}0.736{col 62}{space 4}-.4319538{col 75}{space 3} .6113855
{txt}{space 10}GENDER_MIL {c |}{col 22}{res}{space 2}-17.53863{col 34}{space 2} 5.592892{col 45}{space 1}   -3.14{col 54}{space 3}0.002{col 62}{space 4}-28.50049{col 75}{space 3}-6.576761
{txt}{space 7}FORCE_DENSITY {c |}{col 22}{res}{space 2} 1.926642{col 34}{space 2} .6793794{col 45}{space 1}    2.84{col 54}{space 3}0.005{col 62}{space 4} .5950829{col 75}{space 3} 3.258201
{txt}PKO_FATALITIES_TOTAL {c |}{col 22}{res}{space 2} .0501873{col 34}{space 2} .0296515{col 45}{space 1}    1.69{col 54}{space 3}0.091{col 62}{space 4}-.0079286{col 75}{space 3} .1083032
{txt}{space 12}MANDATE3 {c |}{col 22}{res}{space 2} .5515011{col 34}{space 2} .4091364{col 45}{space 1}    1.35{col 54}{space 3}0.178{col 62}{space 4}-.2503915{col 75}{space 3} 1.353394
{txt}{space 6}ln_KM2_country {c |}{col 22}{res}{space 2} .0693109{col 34}{space 2} .1222033{col 45}{space 1}    0.57{col 54}{space 3}0.571{col 62}{space 4}-.1702032{col 75}{space 3}  .308825
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-.7485487{col 34}{space 2} 1.545768{col 45}{space 1}   -0.48{col 54}{space 3}0.628{col 62}{space 4}-3.778199{col 75}{space 3} 2.281101
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /lnalpha {c |}{col 22}{res}{space 2} .1829067{col 34}{space 2} .2757897{col 62}{space 4}-.3576313{col 75}{space 3} .7234446
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} 1.200702{col 34}{space 2} .3311414{col 62}{space 4} .6993309{col 75}{space 3} 2.061522
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{res}Simulating main parameters.  Please wait....

Note: Clarify is expanding your dataset from 218 observations to 1000
observations in order to accommodate the simulations.  This will append
missing values to the bottom of your original dataset.

% of simulations completed: 11% 22% 33% 44% 55% 66% 77% 88% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9

{com}. preserve
{err}already preserved
{txt}{search r(621), local:r(621);}

{com}. local a =-1 

. setx mean 

. setx CORRUPTION_MIL (`a') 

. simqi, level(95)

{txt}      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
               E(CAT1_MIL) |  {res} 3.214954     1.023968     1.669543     5.42111

{com}. macro list
{txt}{p 0 16}
mrt_seto:{space 7}{res}{res},
{p_end}
{txt}{p 0 16}
mrt_vt:{space 9}{res}{res}(-1) mean mean mean mean mean mean
{p_end}
{txt}{p 0 16}
ML_setes:{space 7}{res}{res}yes
{p_end}
{txt}{p 0 16}
S_2:{space 12}{res}{res}1
{p_end}
{txt}{p 0 16}
S_FNDATE:{space 7}{res}{res} 6 Jun 2019 12:38
{p_end}
{txt}{p 0 16}
S_FN:{space 11}{res}{res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/PKAT_ISQ_rep_data.dta
{p_end}
{txt}{p 0 16}
T_gm_fix_span:{space 2}{res}{res}0
{p_end}
{txt}{p 0 16}
S_level:{space 8}{res}{res}95
{p_end}
{txt}{p 0 16}
F1:{space 13}{res}{res}help advice;
{p_end}
{txt}{p 0 16}
F2:{space 13}{res}{res}describe;
{p_end}
{txt}{p 0 16}
F7:{space 13}{res}{res}save 
{p_end}
{txt}{p 0 16}
F8:{space 13}{res}{res}use 
{p_end}
{txt}{p 0 16}
S_ADO:{space 10}{res}{res}BASE;SITE;.;PERSONAL;PLUS;OLDPLACE
{p_end}
{txt}{p 0 16}
S_FLAVOR:{space 7}{res}{res}Intercooled
{p_end}
{txt}{p 0 16}
S_OS:{space 11}{res}{res}MacOSX
{p_end}
{txt}{p 0 16}
S_OSDTL:{space 8}{res}{res}10.13.6
{p_end}
{txt}{p 0 16}
S_MACH:{space 9}{res}{res}Macintosh (Intel 64-bit)
{p_end}
{txt}{p 0 16}
_a:{space 13}{res}{res}-1
{p_end}

{com}. scalar list 
{txt}     sderr = {res} 1.0239676
{txt}      Pr0U = {res}-4.4211102
{txt}      Pr0L = {res}-.66954309
{txt}       Pr0 = {res} -2.214954
{txt}        Pr = {res}  3.214954
{txt}       PrU = {res} 5.4211102
{txt}       PrL = {res} 1.6695431

{com}. postutil clear

. postfile mypost prediction upper lower using simresults, replace 

. noisily display "start"
{res}start

{com}. set obs 10000 
{txt}{p}
number of observations (_N)  was 1,000,
now 10,000
{p_end}

{com}. while `a' <= 1  {c -(}
{txt}  2{com}. qui simqi , level(95)
{txt}  3{com}. scalar prediction= Pr
{txt}  4{com}. scalar upper = PrU
{txt}  5{com}. scalar lower = PrL
{txt}  6{com}. post mypost (prediction) (upper) (lower)
{txt}  7{com}. scalar drop prediction upper lower
{txt}  8{com}. local a = `a'+.005
{txt}  9{com}. setx CORRUPTION_MIL (`a') 
{txt} 10{com}. display "." _c 
{txt} 11{com}. {c )-}
................................................................................................................................................................................................................................................................................................................................................................................................................
. display ""
{res}

{com}. postclose mypost 

. use simresults, clear 

. sum

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}prediction {c |}{res}        400    1.496321     .735499   .5866086   3.214954
{txt}{space 7}upper {c |}{res}        400    2.309214    1.129812   1.283198    5.42111
{txt}{space 7}lower {c |}{res}        400    .9209902    .4667684   .2405176   1.669543

{com}. gen MV = 0+.005*(_n-1) 

. gsort prediction upper lower -MV 

. graph twoway  line prediction MV, clwidth(medium) clcolor(black) clpattern(solid) sort || line lower MV, clpattern(dash) clwidth(thin) clcolor(black) sort || line upper  MV, clpattern(dash) clwidth(thin) clcolor(black) sort 
{res}
{com}. 
. 
. log off
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/Misconduct_ISQ_replication_log.smcl
  {txt}log type:  {res}smcl
 {txt}paused on:  {res} 7 Jun 2019, 19:53:23
{txt}{.-}
{smcl}
{txt}{sf}{ul off}{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/Misconduct_ISQ_replication_log.smcl
  {txt}log type:  {res}smcl
{txt}resumed on:  {res} 7 Jun 2019, 19:53:44

{com}. *Figure 5. Expected Category 2 allegations among PKO military, by the weighted Political Terror Scores of the PKO military force.

. estsimp nbreg CAT2_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

{txt}Fitting Poisson model:

Iteration 0:{col 16}log pseudolikelihood = {res}-1065.2942{txt}  
Iteration 1:{col 16}log pseudolikelihood = {res} -886.5337{txt}  
Iteration 2:{col 16}log pseudolikelihood = {res}-881.86508{txt}  
Iteration 3:{col 16}log pseudolikelihood = {res}-881.82744{txt}  
Iteration 4:{col 16}log pseudolikelihood = {res}-881.82744{txt}  
{res}
{txt}Fitting constant-only model:

Iteration 0:{col 16}log pseudolikelihood = {res}-486.60088{txt}  
Iteration 1:{col 16}log pseudolikelihood = {res}-430.34183{txt}  
Iteration 2:{col 16}log pseudolikelihood = {res}-429.96601{txt}  
Iteration 3:{col 16}log pseudolikelihood = {res}-429.96574{txt}  
Iteration 4:{col 16}log pseudolikelihood = {res}-429.96574{txt}  
{res}
{txt}Fitting full model:

Iteration 0:{col 16}log pseudolikelihood = {res}-414.85108{txt}  
Iteration 1:{col 16}log pseudolikelihood = {res}-407.02379{txt}  
Iteration 2:{col 16}log pseudolikelihood = {res}-390.30486{txt}  
Iteration 3:{col 16}log pseudolikelihood = {res}-387.85342{txt}  
Iteration 4:{col 16}log pseudolikelihood = {res}-387.82853{txt}  
Iteration 5:{col 16}log pseudolikelihood = {res}-387.82852{txt}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     74.16
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-387.82852{txt}{col 49}Pseudo R2{col 67}= {res}    0.0980

{txt}{ralign 86:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}            CAT2_MIL{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}PTS_MISSION_MIL {c |}{col 22}{res}{space 2} 1.411333{col 34}{space 2} .2286318{col 45}{space 1}    6.17{col 54}{space 3}0.000{col 62}{space 4} .9632231{col 75}{space 3} 1.859443
{txt}{space 6}DEMOCRATIC_MIL {c |}{col 22}{res}{space 2} -.209165{col 34}{space 2} .4052607{col 45}{space 1}   -0.52{col 54}{space 3}0.606{col 62}{space 4}-1.003461{col 75}{space 3} .5851314
{txt}{space 10}GENDER_MIL {c |}{col 22}{res}{space 2} -15.4664{col 34}{space 2} 6.611422{col 45}{space 1}   -2.34{col 54}{space 3}0.019{col 62}{space 4}-28.42455{col 75}{space 3}-2.508253
{txt}{space 7}FORCE_DENSITY {c |}{col 22}{res}{space 2} 1.181652{col 34}{space 2} .5966023{col 45}{space 1}    1.98{col 54}{space 3}0.048{col 62}{space 4} .0123326{col 75}{space 3} 2.350971
{txt}PKO_FATALITIES_TOTAL {c |}{col 22}{res}{space 2} .0862756{col 34}{space 2}  .043194{col 45}{space 1}    2.00{col 54}{space 3}0.046{col 62}{space 4}  .001617{col 75}{space 3} .1709342
{txt}{space 12}MANDATE3 {c |}{col 22}{res}{space 2}-.7774369{col 34}{space 2} .5110782{col 45}{space 1}   -1.52{col 54}{space 3}0.128{col 62}{space 4}-1.779132{col 75}{space 3} .2242579
{txt}{space 6}ln_KM2_country {c |}{col 22}{res}{space 2}-.0103356{col 34}{space 2} .1804494{col 45}{space 1}   -0.06{col 54}{space 3}0.954{col 62}{space 4}-.3640099{col 75}{space 3} .3433387
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-1.055273{col 34}{space 2} 2.218271{col 45}{space 1}   -0.48{col 54}{space 3}0.634{col 62}{space 4}-5.403004{col 75}{space 3} 3.292459
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /lnalpha {c |}{col 22}{res}{space 2} .5267624{col 34}{space 2} .2239695{col 62}{space 4} .0877902{col 75}{space 3} .9657346
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} 1.693441{col 34}{space 2} .3792791{col 62}{space 4} 1.091759{col 75}{space 3} 2.626716
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{res}Simulating main parameters.  Please wait....

Note: Clarify is expanding your dataset from 218 observations to 1000
observations in order to accommodate the simulations.  This will append
missing values to the bottom of your original dataset.

% of simulations completed: 11% 22% 33% 44% 55% 66% 77% 88% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9

{com}. preserve
{err}already preserved
{txt}{search r(621), local:r(621);}

{com}. local a =0 

. setx mean 

. setx PTS_MISSION_MIL (`a') 

. simqi, level(95)

{txt}      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
               E(CAT2_MIL) |  {res}  .245685      .141305     .0775888    .6054061

{com}. macro list
{txt}{p 0 16}
mrt_seto:{space 7}{res}{res},
{p_end}
{txt}{p 0 16}
mrt_vt:{space 9}{res}{res}(0) mean mean mean mean mean mean
{p_end}
{txt}{p 0 16}
ML_setes:{space 7}{res}{res}yes
{p_end}
{txt}{p 0 16}
S_2:{space 12}{res}{res}1
{p_end}
{txt}{p 0 16}
S_FNDATE:{space 7}{res}{res} 6 Jun 2019 12:38
{p_end}
{txt}{p 0 16}
S_FN:{space 11}{res}{res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/PKAT_ISQ_rep_data.dta
{p_end}
{txt}{p 0 16}
T_gm_fix_span:{space 2}{res}{res}0
{p_end}
{txt}{p 0 16}
S_level:{space 8}{res}{res}95
{p_end}
{txt}{p 0 16}
F1:{space 13}{res}{res}help advice;
{p_end}
{txt}{p 0 16}
F2:{space 13}{res}{res}describe;
{p_end}
{txt}{p 0 16}
F7:{space 13}{res}{res}save 
{p_end}
{txt}{p 0 16}
F8:{space 13}{res}{res}use 
{p_end}
{txt}{p 0 16}
S_ADO:{space 10}{res}{res}BASE;SITE;.;PERSONAL;PLUS;OLDPLACE
{p_end}
{txt}{p 0 16}
S_FLAVOR:{space 7}{res}{res}Intercooled
{p_end}
{txt}{p 0 16}
S_OS:{space 11}{res}{res}MacOSX
{p_end}
{txt}{p 0 16}
S_OSDTL:{space 8}{res}{res}10.13.6
{p_end}
{txt}{p 0 16}
S_MACH:{space 9}{res}{res}Macintosh (Intel 64-bit)
{p_end}
{txt}{p 0 16}
_a:{space 13}{res}{res}0
{p_end}

{com}. scalar list 
{txt}     sderr = {res} .14130498
{txt}      Pr0U = {res} .39459389
{txt}      Pr0L = {res} .92241116
{txt}       Pr0 = {res} .75431502
{txt}        Pr = {res} .24568498
{txt}       PrU = {res} .60540611
{txt}       PrL = {res} .07758884

{com}. postutil clear

. postfile mypost prediction upper lower using simresults, replace 

. noisily display "start"
{res}start

{com}. set obs 10000 
{txt}{p}
number of observations (_N)  was 1,000,
now 10,000
{p_end}

{com}. while `a' <= 3  {c -(}
{txt}  2{com}. qui simqi , level(95)
{txt}  3{com}. scalar prediction= Pr
{txt}  4{com}. scalar upper = PrU
{txt}  5{com}. scalar lower = PrL
{txt}  6{com}. post mypost (prediction) (upper) (lower)
{txt}  7{com}. scalar drop prediction upper lower
{txt}  8{com}. local a = `a'+.005
{txt}  9{com}. setx PTS_MISSION_MIL (`a') 
{txt} 10{com}. display "." _c 
{txt} 11{com}. {c )-}
.........................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
. display ""
{res}

{com}. postclose mypost 

. use simresults, clear 

. sum

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}prediction {c |}{res}        601    3.445919    3.715808    .245685   14.76589
{txt}{space 7}upper {c |}{res}        601    5.308155    5.700766   .6054061   23.83316
{txt}{space 7}lower {c |}{res}        601    2.240242    2.510504   .0775888   9.289986

{com}. gen MV = 0+.005*(_n-1) 

. gsort prediction upper lower -MV 

. graph twoway  line prediction MV, clwidth(medium) clcolor(black) clpattern(solid) sort || line lower MV, clpattern(dash) clwidth(thin) clcolor(black) sort || line upper  MV, clpattern(dash) clwidth(thin) clcolor(black) sort 
{res}
{com}. 
. log off
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/Misconduct_ISQ_replication_log.smcl
  {txt}log type:  {res}smcl
 {txt}paused on:  {res} 7 Jun 2019, 19:54:00
{txt}{.-}
{smcl}
{txt}{sf}{ul off}{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/Misconduct_ISQ_replication_log.smcl
  {txt}log type:  {res}smcl
{txt}resumed on:  {res} 7 Jun 2019, 19:54:19

{com}. *Figure 6. Expected Category 2 allegations among PKO military, by the weighted Corruption Scores of the PKO military force.

. **Generate the graph using Model 24, above.

. estsimp nbreg CAT2_MIL CORRUPTION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

{txt}Fitting Poisson model:

Iteration 0:{col 16}log pseudolikelihood = {res}-1107.1862{txt}  
Iteration 1:{col 16}log pseudolikelihood = {res}-900.64656{txt}  
Iteration 2:{col 16}log pseudolikelihood = {res} -895.9236{txt}  
Iteration 3:{col 16}log pseudolikelihood = {res}-895.89124{txt}  
Iteration 4:{col 16}log pseudolikelihood = {res}-895.89124{txt}  
{res}
{txt}Fitting constant-only model:

Iteration 0:{col 16}log pseudolikelihood = {res}-486.60088{txt}  
Iteration 1:{col 16}log pseudolikelihood = {res}-430.34183{txt}  
Iteration 2:{col 16}log pseudolikelihood = {res}-429.96601{txt}  
Iteration 3:{col 16}log pseudolikelihood = {res}-429.96574{txt}  
Iteration 4:{col 16}log pseudolikelihood = {res}-429.96574{txt}  
{res}
{txt}Fitting full model:

Iteration 0:{col 16}log pseudolikelihood = {res} -410.8928{txt}  
Iteration 1:{col 16}log pseudolikelihood = {res} -387.6973{txt}  
Iteration 2:{col 16}log pseudolikelihood = {res}-385.68833{txt}  
Iteration 3:{col 16}log pseudolikelihood = {res}-385.64581{txt}  
Iteration 4:{col 16}log pseudolikelihood = {res}-385.64579{txt}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     96.49
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-385.64579{txt}{col 49}Pseudo R2{col 67}= {res}    0.1031

{txt}{ralign 86:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}            CAT2_MIL{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}CORRUPTION_MIL {c |}{col 22}{res}{space 2} -1.73395{col 34}{space 2} .3580483{col 45}{space 1}   -4.84{col 54}{space 3}0.000{col 62}{space 4}-2.435711{col 75}{space 3}-1.032188
{txt}{space 6}DEMOCRATIC_MIL {c |}{col 22}{res}{space 2}-.4913101{col 34}{space 2} .3838342{col 45}{space 1}   -1.28{col 54}{space 3}0.201{col 62}{space 4}-1.243611{col 75}{space 3} .2609911
{txt}{space 10}GENDER_MIL {c |}{col 22}{res}{space 2}-16.82236{col 34}{space 2} 6.146727{col 45}{space 1}   -2.74{col 54}{space 3}0.006{col 62}{space 4}-28.86972{col 75}{space 3}-4.774992
{txt}{space 7}FORCE_DENSITY {c |}{col 22}{res}{space 2}  1.70379{col 34}{space 2}  .593419{col 45}{space 1}    2.87{col 54}{space 3}0.004{col 62}{space 4} .5407105{col 75}{space 3}  2.86687
{txt}PKO_FATALITIES_TOTAL {c |}{col 22}{res}{space 2}   .09624{col 34}{space 2} .0483441{col 45}{space 1}    1.99{col 54}{space 3}0.047{col 62}{space 4} .0014873{col 75}{space 3} .1909928
{txt}{space 12}MANDATE3 {c |}{col 22}{res}{space 2}-.4736324{col 34}{space 2} .5872188{col 45}{space 1}   -0.81{col 54}{space 3}0.420{col 62}{space 4} -1.62456{col 75}{space 3} .6772953
{txt}{space 6}ln_KM2_country {c |}{col 22}{res}{space 2} .0783161{col 34}{space 2}   .15576{col 45}{space 1}    0.50{col 54}{space 3}0.615{col 62}{space 4}-.2269678{col 75}{space 3}    .3836
{txt}{space 15}_cons {c |}{col 22}{res}{space 2} .6163045{col 34}{space 2} 2.004067{col 45}{space 1}    0.31{col 54}{space 3}0.758{col 62}{space 4}-3.311594{col 75}{space 3} 4.544203
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /lnalpha {c |}{col 22}{res}{space 2} .5017154{col 34}{space 2} .2206496{col 62}{space 4} .0692502{col 75}{space 3} .9341807
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} 1.651552{col 34}{space 2} .3644143{col 62}{space 4} 1.071704{col 75}{space 3} 2.545127
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{res}Simulating main parameters.  Please wait....

Note: Clarify is expanding your dataset from 218 observations to 1000
observations in order to accommodate the simulations.  This will append
missing values to the bottom of your original dataset.

% of simulations completed: 11% 22% 33% 44% 55% 66% 77% 88% 100% 

Number of simulations  : 1000
Names of new variables : b1 b2 b3 b4 b5 b6 b7 b8 b9

{com}. preserve
{err}already preserved
{txt}{search r(621), local:r(621);}

{com}. local a =-1

. setx mean 

. setx CORRUPTION_MIL (`a') 

. simqi, level(95)

{txt}      Quantity of Interest |     Mean       Std. Err.    [95% Conf. Interval]
---------------------------+--------------------------------------------------
               E(CAT2_MIL) |  {res} 24.47875     7.716354      13.3396    42.43136

{com}. macro list
{txt}{p 0 16}
mrt_seto:{space 7}{res}{res},
{p_end}
{txt}{p 0 16}
mrt_vt:{space 9}{res}{res}(-1) mean mean mean mean mean mean
{p_end}
{txt}{p 0 16}
ML_setes:{space 7}{res}{res}yes
{p_end}
{txt}{p 0 16}
S_2:{space 12}{res}{res}1
{p_end}
{txt}{p 0 16}
S_FNDATE:{space 7}{res}{res} 6 Jun 2019 12:38
{p_end}
{txt}{p 0 16}
S_FN:{space 11}{res}{res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/PKAT_ISQ_rep_data.dta
{p_end}
{txt}{p 0 16}
T_gm_fix_span:{space 2}{res}{res}0
{p_end}
{txt}{p 0 16}
S_level:{space 8}{res}{res}95
{p_end}
{txt}{p 0 16}
F1:{space 13}{res}{res}help advice;
{p_end}
{txt}{p 0 16}
F2:{space 13}{res}{res}describe;
{p_end}
{txt}{p 0 16}
F7:{space 13}{res}{res}save 
{p_end}
{txt}{p 0 16}
F8:{space 13}{res}{res}use 
{p_end}
{txt}{p 0 16}
S_ADO:{space 10}{res}{res}BASE;SITE;.;PERSONAL;PLUS;OLDPLACE
{p_end}
{txt}{p 0 16}
S_FLAVOR:{space 7}{res}{res}Intercooled
{p_end}
{txt}{p 0 16}
S_OS:{space 11}{res}{res}MacOSX
{p_end}
{txt}{p 0 16}
S_OSDTL:{space 8}{res}{res}10.13.6
{p_end}
{txt}{p 0 16}
S_MACH:{space 9}{res}{res}Macintosh (Intel 64-bit)
{p_end}
{txt}{p 0 16}
_a:{space 13}{res}{res}-1
{p_end}

{com}. scalar list 
{txt}     sderr = {res}  7.716354
{txt}      Pr0U = {res}-41.431364
{txt}      Pr0L = {res}-12.339603
{txt}       Pr0 = {res}-23.478749
{txt}        Pr = {res} 24.478749
{txt}       PrU = {res} 42.431364
{txt}       PrL = {res} 13.339603

{com}. postutil clear

. postfile mypost prediction upper lower using simresults, replace 

. noisily display "start"
{res}start

{com}. set obs 10000 
{txt}{p}
number of observations (_N)  was 1,000,
now 10,000
{p_end}

{com}. while `a' <= 1  {c -(}
{txt}  2{com}. qui simqi , level(95)
{txt}  3{com}. scalar prediction= Pr
{txt}  4{com}. scalar upper = PrU
{txt}  5{com}. scalar lower = PrL
{txt}  6{com}. post mypost (prediction) (upper) (lower)
{txt}  7{com}. scalar drop prediction upper lower
{txt}  8{com}. local a = `a'+.005
{txt}  9{com}. setx CORRUPTION_MIL (`a') 
{txt} 10{com}. display "." _c 
{txt} 11{com}. {c )-}
................................................................................................................................................................................................................................................................................................................................................................................................................
. display ""
{res}

{com}. postclose mypost 

. use simresults, clear 

. sum

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}prediction {c |}{res}        400    6.763271    6.236973   .8322155   24.47875
{txt}{space 7}upper {c |}{res}        400    10.20085     10.0359   1.844534   42.43137
{txt}{space 7}lower {c |}{res}        400    4.414953    3.864654   .3075866    13.3396

{com}. gen MV = 0+.005*(_n-1) 

. gsort prediction upper lower -MV 

. graph twoway  line prediction MV, clwidth(medium) clcolor(black) clpattern(solid) sort || line lower MV, clpattern(dash) clwidth(thin) clcolor(black) sort || line upper  MV, clpattern(dash) clwidth(thin) clcolor(black) sort 
{res}
{com}. 
. log off
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/Misconduct_ISQ_replication_log.smcl
  {txt}log type:  {res}smcl
 {txt}paused on:  {res} 7 Jun 2019, 19:54:34
{txt}{.-}
{smcl}
{txt}{sf}{ul off}{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/Misconduct_ISQ_replication_log.smcl
  {txt}log type:  {res}smcl
{txt}resumed on:  {res} 7 Jun 2019, 20:05:30

{com}. **Online Appendix A

. *Figure A-1: Distribution of CAT1 allegations including SEAs (Models 1-9)

. hist CAT1_SEA_MIL, freq
{txt}(bin={res}14{txt}, start={res}0{txt}, width={res}6{txt})
{res}
{com}. *Figure A-2: Distribution of CAT1 allegations excluding SEAs (Models 10-18)

. hist CAT1_MIL, freq
{txt}(bin={res}14{txt}, start={res}0{txt}, width={res}3.9285714{txt})
{res}
{com}. *Figure A-3: Distribution of CAT2 allegations (Models 19-27)

. hist CAT2_MIL, freq
{txt}(bin={res}14{txt}, start={res}0{txt}, width={res}10.357143{txt})
{res}
{com}. *Figure A-4: Distribution of Weighted Political Terror Scores for PKO military forces

. hist PTS_MISSION_MIL, freq
{txt}(bin={res}14{txt}, start={res}.13463682{txt}, width={res}.29794526{txt})
{res}
{com}. *Figure A-5: Distribution of Weighted Corruption Scores for PKO military forces

. hist CORRUPTION_MIL, freq
{txt}(bin={res}14{txt}, start={res}-1.2301608{txt}, width={res}.24120864{txt})
{res}
{com}. log off
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/Misconduct_ISQ_replication_log.smcl
  {txt}log type:  {res}smcl
 {txt}paused on:  {res} 7 Jun 2019, 20:06:29
{txt}{.-}
{smcl}
{txt}{sf}{ul off}{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/Misconduct_ISQ_replication_log.smcl
  {txt}log type:  {res}smcl
{txt}resumed on:  {res} 7 Jun 2019, 20:19:10

{com}. *Summary statistics (Table A-3)

. summ CAT1_SEA_MIL if YEAR >2008 & YEAR <2017

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
CAT1_SEA_MIL {c |}{res}        152    4.848684     10.1347          0         71

{com}. 
. summ CAT1_MIL if YEAR >2008 & YEAR <2017

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}CAT1_MIL {c |}{res}        152    2.710526    5.719296          0         52

{com}. 
. summ CAT2_MIL if YEAR >2008 & YEAR <2017

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}CAT2_MIL {c |}{res}        152    8.677632    14.90571          0         80

{com}. 
. summ PTS_MISSION_MIL if YEAR >2008 & YEAR <2017

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
PTS_MISSIO~L {c |}{res}        152    2.080066    .7641832   .1346368    4.30587

{com}. 
. summ DEMOCRATIC_MIL if YEAR >2008 & YEAR <2017

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
DEMOCRATIC~L {c |}{res}        152    .0403844    1.030952  -1.682314   2.567441

{com}. 
. summ CORRUPTION_MIL if YEAR >2008 & YEAR <2017

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
CORRUPTION~L {c |}{res}        152    .0495045    .7086191  -1.230161    2.14676

{com}. 
. summ GDP_MISSION_MIL if YEAR >2008 & YEAR <2017

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
GDP_MISSIO~L {c |}{res}        152     14477.2    14967.71   455.2452   83164.39

{com}. 
. summ GENDER_MIL if YEAR >2008 & YEAR <2017

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}GENDER_MIL {c |}{res}        151    .0384709    .0291601          0   .2117647

{com}. 
. summ FORCE_SIZE if YEAR >2008 & YEAR <2017

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 2}FORCE_SIZE {c |}{res}        152    5815.276    6843.107          1      23448

{com}. 
. summ FORCE_DENSITY if YEAR >2008 & YEAR <2017

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
FORCE_DENS~Y {c |}{res}        152    .0928299    .2528785    .000026   1.218947

{com}. 
. summ PKO_FATALITIES_TOTAL if YEAR >2008 & YEAR <2017

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
PKO_FATALI~L {c |}{res}        152         6.5    11.68573          0        101

{com}. 
. summ MANDATE3 if YEAR >2008 & YEAR <2017

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}MANDATE3 {c |}{res}        152    .5723684    .4963706          0          1

{com}. 
. summ KM2_country if YEAR >2008 & YEAR <2017

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}KM2_country {c |}{res}        152    627468.3    762623.9       9250    2505810

{com}. 
. summ ln_KM2_country if YEAR >2008 & YEAR <2017

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
ln_KM2_cou~y {c |}{res}        152    12.24709    1.790842   9.132379   14.73412

{com}. 
. summ POP_DENSITY if YEAR >2008 & YEAR <2017

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 1}POP_DENSITY {c |}{res}        152    124.5178    142.5672   1.523346   574.8008

{com}. 
. summ GDP_HOST if YEAR >2008 & YEAR <2017

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 4}GDP_HOST {c |}{res}        132    5694.369    9861.003   204.9447   37582.85

{com}. log off
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/Misconduct_ISQ_replication_log.smcl
  {txt}log type:  {res}smcl
 {txt}paused on:  {res} 7 Jun 2019, 20:19:20
{txt}{.-}
{smcl}
{txt}{sf}{ul off}{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/Misconduct_ISQ_replication_log.smcl
  {txt}log type:  {res}smcl
{txt}resumed on:  {res} 7 Jun 2019, 20:24:50

{com}. *Table A-4:  PTS / PTS-Amnesty / PTS-State Comparisons of models depicted in Figures 1, 3, and 5

. *Model 4

. nbreg CAT1_SEA_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-941.83988}  
Iteration 1:{space 3}log pseudolikelihood = {res:-575.53962}  
Iteration 2:{space 3}log pseudolikelihood = {res:-567.09605}  
Iteration 3:{space 3}log pseudolikelihood = {res:-567.05054}  
Iteration 4:{space 3}log pseudolikelihood = {res:-567.05054}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-404.89038}  
Iteration 1:{space 3}log pseudolikelihood = {res:-356.12068}  
Iteration 2:{space 3}log pseudolikelihood = {res:-356.11763}  
Iteration 3:{space 3}log pseudolikelihood = {res:-356.11763}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-346.07002}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-328.21241}  
Iteration 2:{space 3}log pseudolikelihood = {res:  -319.179}  
Iteration 3:{space 3}log pseudolikelihood = {res:-312.32864}  
Iteration 4:{space 3}log pseudolikelihood = {res: -312.2561}  
Iteration 5:{space 3}log pseudolikelihood = {res:-312.25599}  
Iteration 6:{space 3}log pseudolikelihood = {res:-312.25599}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     57.76
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-312.25599{txt}{col 49}Pseudo R2{col 67}= {res}    0.1232

{txt}{ralign 86:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}        CAT1_SEA_MIL{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}PTS_MISSION_MIL {c |}{col 22}{res}{space 2} 1.151343{col 34}{space 2} .3497643{col 45}{space 1}    3.29{col 54}{space 3}0.001{col 62}{space 4} .4658176{col 75}{space 3} 1.836868
{txt}{space 6}DEMOCRATIC_MIL {c |}{col 22}{res}{space 2}-.0379812{col 34}{space 2} .2988585{col 45}{space 1}   -0.13{col 54}{space 3}0.899{col 62}{space 4} -.623733{col 75}{space 3} .5477706
{txt}{space 10}GENDER_MIL {c |}{col 22}{res}{space 2}-16.15391{col 34}{space 2} 8.856818{col 45}{space 1}   -1.82{col 54}{space 3}0.068{col 62}{space 4}-33.51295{col 75}{space 3} 1.205135
{txt}{space 7}FORCE_DENSITY {c |}{col 22}{res}{space 2} 1.473337{col 34}{space 2} .8511924{col 45}{space 1}    1.73{col 54}{space 3}0.083{col 62}{space 4}-.1949696{col 75}{space 3} 3.141643
{txt}PKO_FATALITIES_TOTAL {c |}{col 22}{res}{space 2} .0612265{col 34}{space 2} .0461934{col 45}{space 1}    1.33{col 54}{space 3}0.185{col 62}{space 4} -.029311{col 75}{space 3} .1517639
{txt}{space 12}MANDATE3 {c |}{col 22}{res}{space 2} .1528753{col 34}{space 2} .5410232{col 45}{space 1}    0.28{col 54}{space 3}0.778{col 62}{space 4}-.9075108{col 75}{space 3} 1.213261
{txt}{space 6}ln_KM2_country {c |}{col 22}{res}{space 2} .1153239{col 34}{space 2} .1617285{col 45}{space 1}    0.71{col 54}{space 3}0.476{col 62}{space 4}-.2016582{col 75}{space 3}  .432306
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-3.093769{col 34}{space 2} 1.961723{col 45}{space 1}   -1.58{col 54}{space 3}0.115{col 62}{space 4}-6.938675{col 75}{space 3} .7511381
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/lnalpha {c |}{col 22}{res}{space 2} .4078693{col 34}{space 2} .1947884{col 62}{space 4} .0260909{col 75}{space 3} .7896476
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} 1.503611{col 34}{space 2}  .292886{col 62}{space 4} 1.026434{col 75}{space 3}  2.20262
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 4-Amnesty

. nbreg CAT1_SEA_MIL PTSA_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1160.9102}  
Iteration 1:{space 3}log pseudolikelihood = {res:-662.17098}  
Iteration 2:{space 3}log pseudolikelihood = {res:-482.74655}  
Iteration 3:{space 3}log pseudolikelihood = {res:-476.89935}  
Iteration 4:{space 3}log pseudolikelihood = {res:-476.84351}  
Iteration 5:{space 3}log pseudolikelihood = {res: -476.8435}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-359.67402}  
Iteration 1:{space 3}log pseudolikelihood = {res:-316.88215}  
Iteration 2:{space 3}log pseudolikelihood = {res:-316.88094}  
Iteration 3:{space 3}log pseudolikelihood = {res:-316.88094}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-304.03428}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-285.25699}  
Iteration 2:{space 3}log pseudolikelihood = {res:-270.45059}  
Iteration 3:{space 3}log pseudolikelihood = {res:-269.05933}  
Iteration 4:{space 3}log pseudolikelihood = {res:-269.03422}  
Iteration 5:{space 3}log pseudolikelihood = {res:-269.03422}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       134
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     57.99
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-269.03422{txt}{col 49}Pseudo R2{col 67}= {res}    0.1510

{txt}{ralign 86:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}        CAT1_SEA_MIL{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}PTSA_MIL {c |}{col 22}{res}{space 2} 1.471568{col 34}{space 2} .4088628{col 45}{space 1}    3.60{col 54}{space 3}0.000{col 62}{space 4}  .670212{col 75}{space 3} 2.272925
{txt}{space 6}DEMOCRATIC_MIL {c |}{col 22}{res}{space 2}-.1249042{col 34}{space 2} .2540912{col 45}{space 1}   -0.49{col 54}{space 3}0.623{col 62}{space 4}-.6229139{col 75}{space 3} .3731055
{txt}{space 10}GENDER_MIL {c |}{col 22}{res}{space 2}-10.49471{col 34}{space 2} 7.351736{col 45}{space 1}   -1.43{col 54}{space 3}0.153{col 62}{space 4}-24.90385{col 75}{space 3}  3.91443
{txt}{space 7}FORCE_DENSITY {c |}{col 22}{res}{space 2} 1.229279{col 34}{space 2} .8938777{col 45}{space 1}    1.38{col 54}{space 3}0.169{col 62}{space 4}-.5226892{col 75}{space 3} 2.981247
{txt}PKO_FATALITIES_TOTAL {c |}{col 22}{res}{space 2} .0616149{col 34}{space 2} .0370454{col 45}{space 1}    1.66{col 54}{space 3}0.096{col 62}{space 4}-.0109927{col 75}{space 3} .1342225
{txt}{space 12}MANDATE3 {c |}{col 22}{res}{space 2} .2844232{col 34}{space 2} .4686249{col 45}{space 1}    0.61{col 54}{space 3}0.544{col 62}{space 4}-.6340648{col 75}{space 3} 1.202911
{txt}{space 6}ln_KM2_country {c |}{col 22}{res}{space 2} .0592603{col 34}{space 2} .1986673{col 45}{space 1}    0.30{col 54}{space 3}0.765{col 62}{space 4}-.3301205{col 75}{space 3} .4486411
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-4.430137{col 34}{space 2} 2.729073{col 45}{space 1}   -1.62{col 54}{space 3}0.105{col 62}{space 4}-9.779022{col 75}{space 3}  .918749
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/lnalpha {c |}{col 22}{res}{space 2} .1403514{col 34}{space 2} .2502111{col 62}{space 4}-.3500533{col 75}{space 3} .6307561
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} 1.150678{col 34}{space 2} .2879124{col 62}{space 4} .7046505{col 75}{space 3} 1.879031
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 4-State

. nbreg CAT1_SEA_MIL PTSS_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-769.28355}  
Iteration 1:{space 3}log pseudolikelihood = {res:-575.52136}  
Iteration 2:{space 3}log pseudolikelihood = {res:-571.07582}  
Iteration 3:{space 3}log pseudolikelihood = {res:-571.05945}  
Iteration 4:{space 3}log pseudolikelihood = {res:-571.05945}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-404.89038}  
Iteration 1:{space 3}log pseudolikelihood = {res:-356.12068}  
Iteration 2:{space 3}log pseudolikelihood = {res:-356.11763}  
Iteration 3:{space 3}log pseudolikelihood = {res:-356.11763}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-343.35658}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-325.39134}  
Iteration 2:{space 3}log pseudolikelihood = {res:-317.02582}  
Iteration 3:{space 3}log pseudolikelihood = {res:-309.95813}  
Iteration 4:{space 3}log pseudolikelihood = {res:-309.86637}  
Iteration 5:{space 3}log pseudolikelihood = {res:-309.86605}  
Iteration 6:{space 3}log pseudolikelihood = {res:-309.86605}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     60.59
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-309.86605{txt}{col 49}Pseudo R2{col 67}= {res}    0.1299

{txt}{ralign 86:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}        CAT1_SEA_MIL{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}PTSS_MIL {c |}{col 22}{res}{space 2} 1.219039{col 34}{space 2} .3369812{col 45}{space 1}    3.62{col 54}{space 3}0.000{col 62}{space 4} .5585679{col 75}{space 3}  1.87951
{txt}{space 6}DEMOCRATIC_MIL {c |}{col 22}{res}{space 2}-.0287756{col 34}{space 2} .2718825{col 45}{space 1}   -0.11{col 54}{space 3}0.916{col 62}{space 4}-.5616554{col 75}{space 3} .5041043
{txt}{space 10}GENDER_MIL {c |}{col 22}{res}{space 2}-10.01821{col 34}{space 2}  8.28414{col 45}{space 1}   -1.21{col 54}{space 3}0.227{col 62}{space 4}-26.25483{col 75}{space 3} 6.218403
{txt}{space 7}FORCE_DENSITY {c |}{col 22}{res}{space 2} 1.255125{col 34}{space 2} .8477922{col 45}{space 1}    1.48{col 54}{space 3}0.139{col 62}{space 4}-.4065175{col 75}{space 3} 2.916767
{txt}PKO_FATALITIES_TOTAL {c |}{col 22}{res}{space 2} .0581535{col 34}{space 2} .0390031{col 45}{space 1}    1.49{col 54}{space 3}0.136{col 62}{space 4}-.0182912{col 75}{space 3} .1345983
{txt}{space 12}MANDATE3 {c |}{col 22}{res}{space 2} .2081089{col 34}{space 2} .4741352{col 45}{space 1}    0.44{col 54}{space 3}0.661{col 62}{space 4} -.721179{col 75}{space 3} 1.137397
{txt}{space 6}ln_KM2_country {c |}{col 22}{res}{space 2} .0393175{col 34}{space 2} .1694721{col 45}{space 1}    0.23{col 54}{space 3}0.817{col 62}{space 4}-.2928417{col 75}{space 3} .3714766
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-3.296097{col 34}{space 2} 2.058143{col 45}{space 1}   -1.60{col 54}{space 3}0.109{col 62}{space 4}-7.329984{col 75}{space 3}   .73779
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/lnalpha {c |}{col 22}{res}{space 2} .3573902{col 34}{space 2} .2096367{col 62}{space 4}-.0534902{col 75}{space 3} .7682705
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} 1.429594{col 34}{space 2} .2996952{col 62}{space 4} .9479153{col 75}{space 3} 2.156034
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 13

. nbreg CAT1_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1200.5037}  
Iteration 1:{space 3}log pseudolikelihood = {res: -616.9813}  
Iteration 2:{space 3}log pseudolikelihood = {res:-365.48047}  
Iteration 3:{space 3}log pseudolikelihood = {res:-347.52114}  
Iteration 4:{space 3}log pseudolikelihood = {res:-347.11443}  
Iteration 5:{space 3}log pseudolikelihood = {res:-347.11417}  
Iteration 6:{space 3}log pseudolikelihood = {res:-347.11417}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-327.36556}  
Iteration 1:{space 3}log pseudolikelihood = {res:-297.94672}  
Iteration 2:{space 3}log pseudolikelihood = {res:-297.83249}  
Iteration 3:{space 3}log pseudolikelihood = {res:-297.83248}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-283.05687}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res: -265.0878}  
Iteration 2:{space 3}log pseudolikelihood = {res: -260.9361}  
Iteration 3:{space 3}log pseudolikelihood = {res:-254.66588}  
Iteration 4:{space 3}log pseudolikelihood = {res:-254.45491}  
Iteration 5:{space 3}log pseudolikelihood = {res:-254.45448}  
Iteration 6:{space 3}log pseudolikelihood = {res:-254.45448}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}    108.24
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-254.45448{txt}{col 49}Pseudo R2{col 67}= {res}    0.1456

{txt}{ralign 86:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}            CAT1_MIL{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}PTS_MISSION_MIL {c |}{col 22}{res}{space 2} .9308142{col 34}{space 2} .2664565{col 45}{space 1}    3.49{col 54}{space 3}0.000{col 62}{space 4} .4085689{col 75}{space 3} 1.453059
{txt}{space 6}DEMOCRATIC_MIL {c |}{col 22}{res}{space 2} .1330209{col 34}{space 2} .2156443{col 45}{space 1}    0.62{col 54}{space 3}0.537{col 62}{space 4}-.2896341{col 75}{space 3} .5556759
{txt}{space 10}GENDER_MIL {c |}{col 22}{res}{space 2}-20.97347{col 34}{space 2} 8.029033{col 45}{space 1}   -2.61{col 54}{space 3}0.009{col 62}{space 4}-36.71009{col 75}{space 3}-5.236857
{txt}{space 7}FORCE_DENSITY {c |}{col 22}{res}{space 2} 1.789702{col 34}{space 2} .5630486{col 45}{space 1}    3.18{col 54}{space 3}0.001{col 62}{space 4} .6861467{col 75}{space 3} 2.893257
{txt}PKO_FATALITIES_TOTAL {c |}{col 22}{res}{space 2} .0430086{col 34}{space 2} .0250464{col 45}{space 1}    1.72{col 54}{space 3}0.086{col 62}{space 4}-.0060814{col 75}{space 3} .0920986
{txt}{space 12}MANDATE3 {c |}{col 22}{res}{space 2} .3068129{col 34}{space 2} .3860842{col 45}{space 1}    0.79{col 54}{space 3}0.427{col 62}{space 4}-.4498981{col 75}{space 3} 1.063524
{txt}{space 6}ln_KM2_country {c |}{col 22}{res}{space 2} .0775027{col 34}{space 2} .1110071{col 45}{space 1}    0.70{col 54}{space 3}0.485{col 62}{space 4}-.1400672{col 75}{space 3} .2950725
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-2.547963{col 34}{space 2} 1.408236{col 45}{space 1}   -1.81{col 54}{space 3}0.070{col 62}{space 4}-5.308054{col 75}{space 3} .2121283
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/lnalpha {c |}{col 22}{res}{space 2} .1066773{col 34}{space 2} .1982254{col 62}{space 4}-.2818373{col 75}{space 3} .4951919
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} 1.112575{col 34}{space 2} .2205406{col 62}{space 4} .7543964{col 75}{space 3} 1.640813
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 13-Amnesty

. nbreg CAT1_MIL PTSA_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1259.2204}  
Iteration 1:{space 3}log pseudolikelihood = {res:-559.18687}  
Iteration 2:{space 3}log pseudolikelihood = {res:-374.67065}  
Iteration 3:{space 3}log pseudolikelihood = {res:-301.68623}  
Iteration 4:{space 3}log pseudolikelihood = {res:-299.54915}  
Iteration 5:{space 3}log pseudolikelihood = {res:-299.54466}  
Iteration 6:{space 3}log pseudolikelihood = {res:-299.54466}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-291.25221}  
Iteration 1:{space 3}log pseudolikelihood = {res:-265.25968}  
Iteration 2:{space 3}log pseudolikelihood = {res:-265.18412}  
Iteration 3:{space 3}log pseudolikelihood = {res:-265.18411}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-251.64681}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-234.43303}  
Iteration 2:{space 3}log pseudolikelihood = {res:-222.59391}  
Iteration 3:{space 3}log pseudolikelihood = {res:-221.64028}  
Iteration 4:{space 3}log pseudolikelihood = {res:-221.62481}  
Iteration 5:{space 3}log pseudolikelihood = {res:-221.62481}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       134
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     95.47
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-221.62481{txt}{col 49}Pseudo R2{col 67}= {res}    0.1643

{txt}{ralign 86:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}            CAT1_MIL{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}PTSA_MIL {c |}{col 22}{res}{space 2}  1.06157{col 34}{space 2} .3299236{col 45}{space 1}    3.22{col 54}{space 3}0.001{col 62}{space 4} .4149315{col 75}{space 3} 1.708208
{txt}{space 6}DEMOCRATIC_MIL {c |}{col 22}{res}{space 2} .1668806{col 34}{space 2} .2090365{col 45}{space 1}    0.80{col 54}{space 3}0.425{col 62}{space 4}-.2428235{col 75}{space 3} .5765846
{txt}{space 10}GENDER_MIL {c |}{col 22}{res}{space 2}-14.28885{col 34}{space 2} 6.240577{col 45}{space 1}   -2.29{col 54}{space 3}0.022{col 62}{space 4}-26.52016{col 75}{space 3}-2.057545
{txt}{space 7}FORCE_DENSITY {c |}{col 22}{res}{space 2} 1.536988{col 34}{space 2} .6482334{col 45}{space 1}    2.37{col 54}{space 3}0.018{col 62}{space 4} .2664742{col 75}{space 3} 2.807502
{txt}PKO_FATALITIES_TOTAL {c |}{col 22}{res}{space 2} .0474704{col 34}{space 2} .0245878{col 45}{space 1}    1.93{col 54}{space 3}0.054{col 62}{space 4}-.0007209{col 75}{space 3} .0956616
{txt}{space 12}MANDATE3 {c |}{col 22}{res}{space 2} .3323435{col 34}{space 2} .3350817{col 45}{space 1}    0.99{col 54}{space 3}0.321{col 62}{space 4}-.3244045{col 75}{space 3} .9890915
{txt}{space 6}ln_KM2_country {c |}{col 22}{res}{space 2} .0129373{col 34}{space 2} .1499105{col 45}{space 1}    0.09{col 54}{space 3}0.931{col 62}{space 4}-.2808819{col 75}{space 3} .3067564
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-3.042466{col 34}{space 2} 1.930088{col 45}{space 1}   -1.58{col 54}{space 3}0.115{col 62}{space 4}-6.825369{col 75}{space 3} .7404361
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/lnalpha {c |}{col 22}{res}{space 2}-.0829933{col 34}{space 2} .2708858{col 62}{space 4}-.6139197{col 75}{space 3} .4479332
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} .9203574{col 34}{space 2} .2493117{col 62}{space 4} .5412253{col 75}{space 3} 1.565074
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 13-State

. nbreg CAT1_MIL PTSS_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1144.4165}  
Iteration 1:{space 3}log pseudolikelihood = {res:-570.24047}  
Iteration 2:{space 3}log pseudolikelihood = {res:-374.72215}  
Iteration 3:{space 3}log pseudolikelihood = {res:-348.15894}  
Iteration 4:{space 3}log pseudolikelihood = {res:-347.73385}  
Iteration 5:{space 3}log pseudolikelihood = {res:-347.73333}  
Iteration 6:{space 3}log pseudolikelihood = {res:-347.73333}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-327.36556}  
Iteration 1:{space 3}log pseudolikelihood = {res:-297.94672}  
Iteration 2:{space 3}log pseudolikelihood = {res:-297.83249}  
Iteration 3:{space 3}log pseudolikelihood = {res:-297.83248}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-282.52897}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-264.53987}  
Iteration 2:{space 3}log pseudolikelihood = {res:-261.21353}  
Iteration 3:{space 3}log pseudolikelihood = {res: -253.7953}  
Iteration 4:{space 3}log pseudolikelihood = {res:-253.52793}  
Iteration 5:{space 3}log pseudolikelihood = {res:-253.52662}  
Iteration 6:{space 3}log pseudolikelihood = {res:-253.52662}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}    114.52
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-253.52662{txt}{col 49}Pseudo R2{col 67}= {res}    0.1488

{txt}{ralign 86:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}            CAT1_MIL{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}PTSS_MIL {c |}{col 22}{res}{space 2}  .947337{col 34}{space 2} .2701143{col 45}{space 1}    3.51{col 54}{space 3}0.000{col 62}{space 4} .4179227{col 75}{space 3} 1.476751
{txt}{space 6}DEMOCRATIC_MIL {c |}{col 22}{res}{space 2} .1794425{col 34}{space 2} .2169039{col 45}{space 1}    0.83{col 54}{space 3}0.408{col 62}{space 4}-.2456813{col 75}{space 3} .6045664
{txt}{space 10}GENDER_MIL {c |}{col 22}{res}{space 2}-15.43847{col 34}{space 2} 7.609558{col 45}{space 1}   -2.03{col 54}{space 3}0.042{col 62}{space 4}-30.35293{col 75}{space 3}-.5240095
{txt}{space 7}FORCE_DENSITY {c |}{col 22}{res}{space 2} 1.545837{col 34}{space 2} .5931671{col 45}{space 1}    2.61{col 54}{space 3}0.009{col 62}{space 4} .3832512{col 75}{space 3} 2.708423
{txt}PKO_FATALITIES_TOTAL {c |}{col 22}{res}{space 2} .0413849{col 34}{space 2}  .021229{col 45}{space 1}    1.95{col 54}{space 3}0.051{col 62}{space 4}-.0002231{col 75}{space 3}  .082993
{txt}{space 12}MANDATE3 {c |}{col 22}{res}{space 2} .2582035{col 34}{space 2} .3720441{col 45}{space 1}    0.69{col 54}{space 3}0.488{col 62}{space 4}-.4709896{col 75}{space 3} .9873965
{txt}{space 6}ln_KM2_country {c |}{col 22}{res}{space 2} .0050983{col 34}{space 2} .1266094{col 45}{space 1}    0.04{col 54}{space 3}0.968{col 62}{space 4}-.2430515{col 75}{space 3} .2532482
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-2.419933{col 34}{space 2} 1.578509{col 45}{space 1}   -1.53{col 54}{space 3}0.125{col 62}{space 4}-5.513753{col 75}{space 3} .6738877
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/lnalpha {c |}{col 22}{res}{space 2} .0817406{col 34}{space 2} .2344007{col 62}{space 4}-.3776763{col 75}{space 3} .5411576
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} 1.085174{col 34}{space 2} .2543657{col 62}{space 4} .6854523{col 75}{space 3} 1.717995
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 22

. nbreg CAT2_MIL PTS_MISSION_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1065.2942}  
Iteration 1:{space 3}log pseudolikelihood = {res: -886.5337}  
Iteration 2:{space 3}log pseudolikelihood = {res:-881.86508}  
Iteration 3:{space 3}log pseudolikelihood = {res:-881.82744}  
Iteration 4:{space 3}log pseudolikelihood = {res:-881.82744}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-486.60088}  
Iteration 1:{space 3}log pseudolikelihood = {res:-430.34183}  
Iteration 2:{space 3}log pseudolikelihood = {res:-429.96601}  
Iteration 3:{space 3}log pseudolikelihood = {res:-429.96574}  
Iteration 4:{space 3}log pseudolikelihood = {res:-429.96574}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-414.85108}  
Iteration 1:{space 3}log pseudolikelihood = {res:-407.02379}  
Iteration 2:{space 3}log pseudolikelihood = {res:-390.30486}  
Iteration 3:{space 3}log pseudolikelihood = {res:-387.85342}  
Iteration 4:{space 3}log pseudolikelihood = {res:-387.82853}  
Iteration 5:{space 3}log pseudolikelihood = {res:-387.82852}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     74.16
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-387.82852{txt}{col 49}Pseudo R2{col 67}= {res}    0.0980

{txt}{ralign 86:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}            CAT2_MIL{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}PTS_MISSION_MIL {c |}{col 22}{res}{space 2} 1.411333{col 34}{space 2} .2286318{col 45}{space 1}    6.17{col 54}{space 3}0.000{col 62}{space 4} .9632231{col 75}{space 3} 1.859443
{txt}{space 6}DEMOCRATIC_MIL {c |}{col 22}{res}{space 2} -.209165{col 34}{space 2} .4052607{col 45}{space 1}   -0.52{col 54}{space 3}0.606{col 62}{space 4}-1.003461{col 75}{space 3} .5851314
{txt}{space 10}GENDER_MIL {c |}{col 22}{res}{space 2} -15.4664{col 34}{space 2} 6.611422{col 45}{space 1}   -2.34{col 54}{space 3}0.019{col 62}{space 4}-28.42455{col 75}{space 3}-2.508253
{txt}{space 7}FORCE_DENSITY {c |}{col 22}{res}{space 2} 1.181652{col 34}{space 2} .5966023{col 45}{space 1}    1.98{col 54}{space 3}0.048{col 62}{space 4} .0123326{col 75}{space 3} 2.350971
{txt}PKO_FATALITIES_TOTAL {c |}{col 22}{res}{space 2} .0862756{col 34}{space 2}  .043194{col 45}{space 1}    2.00{col 54}{space 3}0.046{col 62}{space 4}  .001617{col 75}{space 3} .1709342
{txt}{space 12}MANDATE3 {c |}{col 22}{res}{space 2}-.7774369{col 34}{space 2} .5110782{col 45}{space 1}   -1.52{col 54}{space 3}0.128{col 62}{space 4}-1.779132{col 75}{space 3} .2242579
{txt}{space 6}ln_KM2_country {c |}{col 22}{res}{space 2}-.0103356{col 34}{space 2} .1804494{col 45}{space 1}   -0.06{col 54}{space 3}0.954{col 62}{space 4}-.3640099{col 75}{space 3} .3433387
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-1.055273{col 34}{space 2} 2.218271{col 45}{space 1}   -0.48{col 54}{space 3}0.634{col 62}{space 4}-5.403004{col 75}{space 3} 3.292459
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/lnalpha {c |}{col 22}{res}{space 2} .5267624{col 34}{space 2} .2239695{col 62}{space 4} .0877902{col 75}{space 3} .9657346
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} 1.693441{col 34}{space 2} .3792791{col 62}{space 4} 1.091759{col 75}{space 3} 2.626716
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 22-Amnesty

. nbreg CAT2_MIL PTSA_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-980.94339}  
Iteration 1:{space 3}log pseudolikelihood = {res:-766.99531}  
Iteration 2:{space 3}log pseudolikelihood = {res: -763.5252}  
Iteration 3:{space 3}log pseudolikelihood = {res:-763.50383}  
Iteration 4:{space 3}log pseudolikelihood = {res:-763.50383}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-438.94097}  
Iteration 1:{space 3}log pseudolikelihood = {res:-382.27166}  
Iteration 2:{space 3}log pseudolikelihood = {res:-381.93999}  
Iteration 3:{space 3}log pseudolikelihood = {res:-381.93967}  
Iteration 4:{space 3}log pseudolikelihood = {res:-381.93967}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-369.10115}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-356.96166}  
Iteration 2:{space 3}log pseudolikelihood = {res:-345.05163}  
Iteration 3:{space 3}log pseudolikelihood = {res:-342.73189}  
Iteration 4:{space 3}log pseudolikelihood = {res:-342.68245}  
Iteration 5:{space 3}log pseudolikelihood = {res:-342.68241}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       134
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     58.35
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-342.68241{txt}{col 49}Pseudo R2{col 67}= {res}    0.1028

{txt}{ralign 86:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}            CAT2_MIL{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}PTSA_MIL {c |}{col 22}{res}{space 2} 1.709204{col 34}{space 2} .3128574{col 45}{space 1}    5.46{col 54}{space 3}0.000{col 62}{space 4} 1.096014{col 75}{space 3} 2.322393
{txt}{space 6}DEMOCRATIC_MIL {c |}{col 22}{res}{space 2}-.3574902{col 34}{space 2} .3100359{col 45}{space 1}   -1.15{col 54}{space 3}0.249{col 62}{space 4}-.9651493{col 75}{space 3} .2501689
{txt}{space 10}GENDER_MIL {c |}{col 22}{res}{space 2}-4.853505{col 34}{space 2}  7.45986{col 45}{space 1}   -0.65{col 54}{space 3}0.515{col 62}{space 4}-19.47456{col 75}{space 3} 9.767552
{txt}{space 7}FORCE_DENSITY {c |}{col 22}{res}{space 2} .7172955{col 34}{space 2} .7000128{col 45}{space 1}    1.02{col 54}{space 3}0.306{col 62}{space 4}-.6547044{col 75}{space 3} 2.089295
{txt}PKO_FATALITIES_TOTAL {c |}{col 22}{res}{space 2} .0964773{col 34}{space 2} .0432262{col 45}{space 1}    2.23{col 54}{space 3}0.026{col 62}{space 4} .0117554{col 75}{space 3} .1811991
{txt}{space 12}MANDATE3 {c |}{col 22}{res}{space 2}-.8110771{col 34}{space 2} .4565692{col 45}{space 1}   -1.78{col 54}{space 3}0.076{col 62}{space 4}-1.705936{col 75}{space 3} .0837821
{txt}{space 6}ln_KM2_country {c |}{col 22}{res}{space 2}-.1145847{col 34}{space 2} .2179843{col 45}{space 1}   -0.53{col 54}{space 3}0.599{col 62}{space 4}-.5418261{col 75}{space 3} .3126567
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}  -1.8898{col 34}{space 2} 2.670905{col 45}{space 1}   -0.71{col 54}{space 3}0.479{col 62}{space 4}-7.124677{col 75}{space 3} 3.345077
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/lnalpha {c |}{col 22}{res}{space 2} .5564463{col 34}{space 2} .2286694{col 62}{space 4} .1082626{col 75}{space 3}  1.00463
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} 1.744462{col 34}{space 2} .3989051{col 62}{space 4}  1.11434{col 75}{space 3} 2.730897
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Model 22-State

. nbreg CAT2_MIL PTSS_MIL DEMOCRATIC_MIL GENDER_MIL FORCE_DENSITY PKO_FATALITIES_TOTAL MANDATE3 ln_KM2_country, cluster(ID)

{txt}Fitting Poisson model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-869.00453}  
Iteration 1:{space 3}log pseudolikelihood = {res:-772.40295}  
Iteration 2:{space 3}log pseudolikelihood = {res:-770.37431}  
Iteration 3:{space 3}log pseudolikelihood = {res:-770.37041}  
Iteration 4:{space 3}log pseudolikelihood = {res:-770.37041}  
{res}
{txt}Fitting constant-only model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-486.60088}  
Iteration 1:{space 3}log pseudolikelihood = {res:-430.34183}  
Iteration 2:{space 3}log pseudolikelihood = {res:-429.96601}  
Iteration 3:{space 3}log pseudolikelihood = {res:-429.96574}  
Iteration 4:{space 3}log pseudolikelihood = {res:-429.96574}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-410.26003}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-393.25676}  
Iteration 2:{space 3}log pseudolikelihood = {res:-383.06394}  
Iteration 3:{space 3}log pseudolikelihood = {res:-377.61366}  
Iteration 4:{space 3}log pseudolikelihood = {res:-377.59048}  
Iteration 5:{space 3}log pseudolikelihood = {res:-377.59047}  
{res}
{txt}Negative binomial regression{col 49}Number of obs{col 67}= {res}       151
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     82.27
{txt}{col 1}Dispersion{col 22}= {res}mean{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-377.59047{txt}{col 49}Pseudo R2{col 67}= {res}    0.1218

{txt}{ralign 86:(Std. Err. adjusted for {res:29} clusters in ID)}
{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}            CAT2_MIL{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}PTSS_MIL {c |}{col 22}{res}{space 2} 1.716459{col 34}{space 2}  .287264{col 45}{space 1}    5.98{col 54}{space 3}0.000{col 62}{space 4} 1.153432{col 75}{space 3} 2.279486
{txt}{space 6}DEMOCRATIC_MIL {c |}{col 22}{res}{space 2}-.3617865{col 34}{space 2} .3111664{col 45}{space 1}   -1.16{col 54}{space 3}0.245{col 62}{space 4}-.9716613{col 75}{space 3} .2480884
{txt}{space 10}GENDER_MIL {c |}{col 22}{res}{space 2}-6.231696{col 34}{space 2} 5.636585{col 45}{space 1}   -1.11{col 54}{space 3}0.269{col 62}{space 4} -17.2792{col 75}{space 3} 4.815808
{txt}{space 7}FORCE_DENSITY {c |}{col 22}{res}{space 2} 1.126942{col 34}{space 2} .5787837{col 45}{space 1}    1.95{col 54}{space 3}0.052{col 62}{space 4}-.0074533{col 75}{space 3} 2.261337
{txt}PKO_FATALITIES_TOTAL {c |}{col 22}{res}{space 2} .0702689{col 34}{space 2} .0343293{col 45}{space 1}    2.05{col 54}{space 3}0.041{col 62}{space 4} .0029848{col 75}{space 3} .1375531
{txt}{space 12}MANDATE3 {c |}{col 22}{res}{space 2}-.6516941{col 34}{space 2} .3882907{col 45}{space 1}   -1.68{col 54}{space 3}0.093{col 62}{space 4} -1.41273{col 75}{space 3} .1093417
{txt}{space 6}ln_KM2_country {c |}{col 22}{res}{space 2}-.0416826{col 34}{space 2} .1855928{col 45}{space 1}   -0.22{col 54}{space 3}0.822{col 62}{space 4}-.4054378{col 75}{space 3} .3220725
{txt}{space 15}_cons {c |}{col 22}{res}{space 2}-2.788806{col 34}{space 2} 2.176362{col 45}{space 1}   -1.28{col 54}{space 3}0.200{col 62}{space 4}-7.054397{col 75}{space 3} 1.476785
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 12}/lnalpha {c |}{col 22}{res}{space 2} .3000016{col 34}{space 2} .2086163{col 62}{space 4}-.1088789{col 75}{space 3}  .708882
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               alpha {c |}{col 22}{res}{space 2} 1.349861{col 34}{space 2}  .281603{col 62}{space 4} .8968391{col 75}{space 3} 2.031718
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. log off
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/Misconduct_ISQ_replication_log.smcl
  {txt}log type:  {res}smcl
 {txt}paused on:  {res} 7 Jun 2019, 20:25:00
{txt}{.-}
{smcl}
{txt}{sf}{ul off}{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/Misconduct_ISQ_replication_log.smcl
  {txt}log type:  {res}smcl
{txt}resumed on:  {res} 7 Jun 2019, 20:52:44

{com}. *Table A-5: Summary statistics for PTS & Corruption by Mission

. 
. summ PTS_MISSION_MIL if YEAR >2008 & YEAR <2017 & ID ==1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
PTS_MISSIO~L {c |}{res}          2    1.972649    .1238734   1.885057   2.060241

{com}. 
. summ CORRUPTION_MIL if YEAR >2008 & YEAR <2017 & ID ==1

{txt}    Variable {c |}        Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 57}
CORRUPTION~L {c |}{res}          2   -.0403657    .0214908   -.055562  -.0251694

{com}. 
. *and so forth, across mission IDs. The MISSION variable gives PKO mission names and locations.

. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}/Users/cale.horne/Desktop/PKAT/Misconduct paper/Final version/Misconduct_ISQ_replication_log.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 7 Jun 2019, 20:52:52
{txt}{.-}
{smcl}
{txt}{sf}{ul off}